Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117548
Brendan Klare, A. Paulino, Anil K. Jain
A study of the distinctiveness of different facial features (MLBP, SIFT, and facial marks) with respect to distinguishing identical twins is presented. The accuracy of distinguishing between identical twin pairs is measured using the entire face, as well as each facial component (eyes, eyebrows, nose, and mouth). The impact of discriminant learning methods on twin face recognition is investigated. Experimental results indicate that features that perform well in distinguishing identical twins are not always consistent with the features that best distinguish two non-twin faces.
{"title":"Analysis of facial features in identical twins","authors":"Brendan Klare, A. Paulino, Anil K. Jain","doi":"10.1109/IJCB.2011.6117548","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117548","url":null,"abstract":"A study of the distinctiveness of different facial features (MLBP, SIFT, and facial marks) with respect to distinguishing identical twins is presented. The accuracy of distinguishing between identical twin pairs is measured using the entire face, as well as each facial component (eyes, eyebrows, nose, and mouth). The impact of discriminant learning methods on twin face recognition is investigated. Experimental results indicate that features that perform well in distinguishing identical twins are not always consistent with the features that best distinguish two non-twin faces.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121859538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117475
Rubisley de P. Lemes, O. Bellon, Luciano Silva, Anil K. Jain
We present some results on newborn identification through high-resolution images of palmar surfaces. To our knowledge, there is no biometric system currently available that can be effectively used for newborn identification. The manual procedure of capturing inked footprints in practice for this purpose is limited for use inside hospitals and is not an effective solution for identification purposes. The use of friction ridge patterns on the hands of newborns is challenging due to both the small size of newborn's papillary ridges, which are, on average, 2.5 to 3 times smaller than the ridges in adult fingerprints, and their fragility, making them amenable to deformation. The proposed palmprint based automatic system for newborn identification is relatively easy to use and shows the feasibility of this approach. Experiments were performed on images collected from 250 newborns at the University Hospital (Universidade Federal do Paraná). An image acquisition protocol was developed in order to collect suitable images. When considering the good quality palmar images, the results show that the proposed approach is promising.
我们提出了一些通过手掌表面的高分辨率图像识别新生儿的结果。据我们所知，目前还没有可以有效用于新生儿识别的生物识别系统。在实践中，为这一目的而手动捕获油墨足迹的程序仅限于在医院内部使用，并且不是用于识别目的的有效解决方案。在新生儿的手上使用摩擦脊图案是具有挑战性的，因为新生儿的乳头脊尺寸小，平均比成人指纹的脊小2.5到3倍，而且它们很脆弱，使它们易于变形。所提出的基于掌纹的新生儿自动识别系统使用相对简单，表明了该方法的可行性。实验是在大学医院(universsidade Federal do paranar)收集的250名新生儿的图像上进行的。为了采集合适的图像，开发了图像采集协议。当考虑到高质量的手掌图像时，结果表明该方法是有前途的。
{"title":"Biometric recognition of newborns: Identification using palmprints","authors":"Rubisley de P. Lemes, O. Bellon, Luciano Silva, Anil K. Jain","doi":"10.1109/IJCB.2011.6117475","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117475","url":null,"abstract":"We present some results on newborn identification through high-resolution images of palmar surfaces. To our knowledge, there is no biometric system currently available that can be effectively used for newborn identification. The manual procedure of capturing inked footprints in practice for this purpose is limited for use inside hospitals and is not an effective solution for identification purposes. The use of friction ridge patterns on the hands of newborns is challenging due to both the small size of newborn's papillary ridges, which are, on average, 2.5 to 3 times smaller than the ridges in adult fingerprints, and their fragility, making them amenable to deformation. The proposed palmprint based automatic system for newborn identification is relatively easy to use and shows the feasibility of this approach. Experiments were performed on images collected from 250 newborns at the University Hospital (Universidade Federal do Paraná). An image acquisition protocol was developed in order to collect suitable images. When considering the good quality palmar images, the results show that the proposed approach is promising.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114299174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117521
Omar Ocegueda, G. Passalis, T. Theoharis, S. Shah, I. Kakadiaris
We present a novel approach for computing a compact and highly discriminant biometric signature for 3D face recognition using linear dimensionality reduction techniques. Initially, a geometry-image representation is used to effectively resample the raw 3D data. Subsequently, a wavelet transform is applied and a biometric signature composed of 7,200 wavelet coefficients is extracted. Finally, we apply a second linear dimensionality reduction step to the wavelet coefficients using Linear Discriminant Analysis and compute a compact biometric signature. Although this biometric signature consists of just 57 coefficients, it is highly discriminant. Our approach, UR3D-C, is experimentally validated using four publicly available databases (FRGC v1, FRGC v2, Bosphorus and BU-3DFE). State-of-the-art performance is reported in all of the above databases.
{"title":"UR3D-C: Linear dimensionality reduction for efficient 3D face recognition","authors":"Omar Ocegueda, G. Passalis, T. Theoharis, S. Shah, I. Kakadiaris","doi":"10.1109/IJCB.2011.6117521","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117521","url":null,"abstract":"We present a novel approach for computing a compact and highly discriminant biometric signature for 3D face recognition using linear dimensionality reduction techniques. Initially, a geometry-image representation is used to effectively resample the raw 3D data. Subsequently, a wavelet transform is applied and a biometric signature composed of 7,200 wavelet coefficients is extracted. Finally, we apply a second linear dimensionality reduction step to the wavelet coefficients using Linear Discriminant Analysis and compute a compact biometric signature. Although this biometric signature consists of just 57 coefficients, it is highly discriminant. Our approach, UR3D-C, is experimentally validated using four publicly available databases (FRGC v1, FRGC v2, Bosphorus and BU-3DFE). State-of-the-art performance is reported in all of the above databases.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117596
G. Santos, Hugo Proença
Corner detection has motivated a great deal of research and is particularly important in a variety of tasks related to computer vision, acting as a basis for further stages. In particular, the detection of eye-corners in facial images is important in applications in biometric systems and assisted-driving systems. We empirically evaluated the state-of-the-art of eye-corner detection proposals and found that they achieve satisfactory results only when dealing with high-quality data. Hence, in this paper, we describe an eye-corner detection method that emphasizes robustness, i.e., its ability to deal with degraded data, and applicability to real-world conditions. Our experiments show that the proposed method outperforms others in both noise-free and degraded data (blurred and rotated images and images with significant variations in scale), which is a major achievement.
{"title":"A robust eye-corner detection method for real-world data","authors":"G. Santos, Hugo Proença","doi":"10.1109/IJCB.2011.6117596","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117596","url":null,"abstract":"Corner detection has motivated a great deal of research and is particularly important in a variety of tasks related to computer vision, acting as a basis for further stages. In particular, the detection of eye-corners in facial images is important in applications in biometric systems and assisted-driving systems. We empirically evaluated the state-of-the-art of eye-corner detection proposals and found that they achieve satisfactory results only when dealing with high-quality data. Hence, in this paper, we describe an eye-corner detection method that emphasizes robustness, i.e., its ability to deal with degraded data, and applicability to real-world conditions. Our experiments show that the proposed method outperforms others in both noise-free and degraded data (blurred and rotated images and images with significant variations in scale), which is a major achievement.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132879271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117546
Ruifang Wang, D. Ramos, Julian Fierrez
In forensic applications the evidential value of palmprints is obvious according to surveys of law enforcement agencies which indicate that 30 percent of the latents recovered from crime scenes are from palms. Consequently, developing forensic automatic palmprint identification technology is an urgent and challenging task which deals with latent (i.e., partial) and full palmprints captured or recovered at 500 ppi at least (the current standard in forensic applications) for minutiae-based offline recognition. Moreover, a rigorous quantification of the evidential value of biometrics, such as fingerprints and palmprints, is essential in modern forensic science. Recently, radial triangulation has been proposed as a step towards this objective in fingerprints, using minutiae manually extracted by experts. In this work we help in automatizing such comparison strategy, and generalize it to palmprints. Firstly, palmprint segmentation and enhancement are implemented for full prints feature extraction by a commercial biometric SDK in an automatic way, while features of latent prints are manually extracted by forensic experts. Then a latent-to-full palmprint comparison algorithm based on radial triangulation is proposed, in which radial triangulation is utilized for minutiae modeling. Finally, 22 latent palmprints from real forensic cases and 8680 full palmprints from criminal investigation field are used for performance evaluation. Experimental results proof the usability and efficiency of the proposed system, i.e, rank-1 identification rate of 62% is achieved despite the inherent difficulty of latent-to-full
{"title":"Latent-to-full palmprint comparison based on radial triangulation under forensic conditions","authors":"Ruifang Wang, D. Ramos, Julian Fierrez","doi":"10.1109/IJCB.2011.6117546","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117546","url":null,"abstract":"In forensic applications the evidential value of palmprints is obvious according to surveys of law enforcement agencies which indicate that 30 percent of the latents recovered from crime scenes are from palms. Consequently, developing forensic automatic palmprint identification technology is an urgent and challenging task which deals with latent (i.e., partial) and full palmprints captured or recovered at 500 ppi at least (the current standard in forensic applications) for minutiae-based offline recognition. Moreover, a rigorous quantification of the evidential value of biometrics, such as fingerprints and palmprints, is essential in modern forensic science. Recently, radial triangulation has been proposed as a step towards this objective in fingerprints, using minutiae manually extracted by experts. In this work we help in automatizing such comparison strategy, and generalize it to palmprints. Firstly, palmprint segmentation and enhancement are implemented for full prints feature extraction by a commercial biometric SDK in an automatic way, while features of latent prints are manually extracted by forensic experts. Then a latent-to-full palmprint comparison algorithm based on radial triangulation is proposed, in which radial triangulation is utilized for minutiae modeling. Finally, 22 latent palmprints from real forensic cases and 8680 full palmprints from criminal investigation field are used for performance evaluation. Experimental results proof the usability and efficiency of the proposed system, i.e, rank-1 identification rate of 62% is achieved despite the inherent difficulty of latent-to-full","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122215443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117600
Felix Juefei-Xu, Khoa Luu, M. Savvides, T. D. Bui, C. Suen
In this paper, we will present a novel framework of utilizing periocular region for age invariant face recognition. To obtain age invariant features, we first perform preprocessing schemes, such as pose correction, illumination and periocular region normalization. And then we apply robust Walsh-Hadamard transform encoded local binary patterns (WLBP) on preprocessed periocular region only. We find the WLBP feature on periocular region maintains consistency of the same individual across ages. Finally, we use unsupervised discriminant projection (UDP) to build subspaces on WLBP featured periocular images and gain 100% rank-1 identification rate and 98% verification rate at 0.1% false accept rate on the entire FG-NET database. Compared to published results, our proposed approach yields the best recognition and identification results.
{"title":"Investigating age invariant face recognition based on periocular biometrics","authors":"Felix Juefei-Xu, Khoa Luu, M. Savvides, T. D. Bui, C. Suen","doi":"10.1109/IJCB.2011.6117600","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117600","url":null,"abstract":"In this paper, we will present a novel framework of utilizing periocular region for age invariant face recognition. To obtain age invariant features, we first perform preprocessing schemes, such as pose correction, illumination and periocular region normalization. And then we apply robust Walsh-Hadamard transform encoded local binary patterns (WLBP) on preprocessed periocular region only. We find the WLBP feature on periocular region maintains consistency of the same individual across ages. Finally, we use unsupervised discriminant projection (UDP) to build subspaces on WLBP featured periocular images and gain 100% rank-1 identification rate and 98% verification rate at 0.1% false accept rate on the entire FG-NET database. Compared to published results, our proposed approach yields the best recognition and identification results.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121954701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117523
R. Kumar, B. Bhanu, Subir Ghosh, N. Thakoor
The performance of a recognition system is usually experimentally determined. Therefore, one cannot predict the performance of a recognition system a priori for a new dataset. In this paper, a statistical model to predict the value of k in the rank-k identification rate for a given biometric system is presented. Thus, one needs to search only the topmost k match scores to locate the true match object. A geometrical probability distribution is used to model the number of non match scores present in the set of similarity scores. The model is tested in simulation and by using a public dataset. The model is also indirectly validated against the previously published results. The actual results obtained using publicly available database are very close to the predicted results which validates the proposed model.
{"title":"Prediction and validation of indexing performance for biometrics","authors":"R. Kumar, B. Bhanu, Subir Ghosh, N. Thakoor","doi":"10.1109/IJCB.2011.6117523","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117523","url":null,"abstract":"The performance of a recognition system is usually experimentally determined. Therefore, one cannot predict the performance of a recognition system a priori for a new dataset. In this paper, a statistical model to predict the value of k in the rank-k identification rate for a given biometric system is presented. Thus, one needs to search only the topmost k match scores to locate the true match object. A geometrical probability distribution is used to model the number of non match scores present in the set of similarity scores. The model is tested in simulation and by using a public dataset. The model is also indirectly validated against the previously published results. The actual results obtained using publicly available database are very close to the predicted results which validates the proposed model.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127810236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117533
Devansh Arpit, A. Namboodiri
This paper deals with extraction of fingerprint features directly from gray scale images by the method of ridge tracing. While doing so, we make substantial use of contextual information gathered during the tracing process. Narrow bandpass based filtering methods for fingerprint image enhancement are extremely robust as noisy regions do not affect the result of cleaner ones. However, these method often generate artifacts whenever the underlying image does not fit the filter model, which may be due to the presence of noise and singularities. The proposed method allows us to use the contextual information to better handle such noisy regions. Moreover, the various parameters used in the algorithm have been made adaptive in order to circumvent human supervision. The experimental results from our algorithm have been compared with those from Gabor based filtering and feature extraction, as well as with the original ridge tracing work from Maio and Maltoni [11]. The results clearly indicate that the proposed approach makes ridge tracing more robust to noise and makes the extracted features more reliable.
{"title":"Fingerprint feature extraction from gray scale images by ridge tracing","authors":"Devansh Arpit, A. Namboodiri","doi":"10.1109/IJCB.2011.6117533","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117533","url":null,"abstract":"This paper deals with extraction of fingerprint features directly from gray scale images by the method of ridge tracing. While doing so, we make substantial use of contextual information gathered during the tracing process. Narrow bandpass based filtering methods for fingerprint image enhancement are extremely robust as noisy regions do not affect the result of cleaner ones. However, these method often generate artifacts whenever the underlying image does not fit the filter model, which may be due to the presence of noise and singularities. The proposed method allows us to use the contextual information to better handle such noisy regions. Moreover, the various parameters used in the algorithm have been made adaptive in order to circumvent human supervision. The experimental results from our algorithm have been compared with those from Gabor based filtering and feature extraction, as well as with the original ridge tracing work from Maio and Maltoni [11]. The results clearly indicate that the proposed approach makes ridge tracing more robust to noise and makes the extracted features more reliable.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115519476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1049/iet-bmt.2011.0007
Kenta Takahashi, Ken Naganuma
The Correlation Invariant Random Filtering (or CIRF) is an algorithm for cancelable biometrics, and known to have provable security. However, the security proof requires a strong assumption with regard to biometric features, which is rarely satisfied in practice. In this paper we examine the security of the CIRF when the assumption is not satisfied, and show that there are problems in secrecy of the feature and diversity of cancelable templates. To address these problems, we interpret the CIRF from an algebraic point of view, and generalize it based on a quotient polynomial ring. Then we prove several theorems which derive a new transformation algorithm for cancelable biometrics. The proposed algorithm has provable security without any condition of biometric features.
{"title":"Unconditionally provably secure cancelable biometrics based on a quotient polynomial ring","authors":"Kenta Takahashi, Ken Naganuma","doi":"10.1049/iet-bmt.2011.0007","DOIUrl":"https://doi.org/10.1049/iet-bmt.2011.0007","url":null,"abstract":"The Correlation Invariant Random Filtering (or CIRF) is an algorithm for cancelable biometrics, and known to have provable security. However, the security proof requires a strong assumption with regard to biometric features, which is rarely satisfied in practice. In this paper we examine the security of the CIRF when the assumption is not satisfied, and show that there are problems in secrecy of the feature and diversity of cancelable templates. To address these problems, we interpret the CIRF from an algebraic point of view, and generalize it based on a quotient polynomial ring. Then we prove several theorems which derive a new transformation algorithm for cancelable biometrics. The proposed algorithm has provable security without any condition of biometric features.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116233840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-10-11DOI: 10.1109/IJCB.2011.6117545
Sumit Shekhar, Vishal M. Patel, R. Chellappa
Recognition of low resolution face images is a challenging problem in many practical face recognition systems. Methods have been proposed in the face recognition literature for the problem when the probe is of low resolution, and a high resolution gallery is available for recognition. These methods modify the probe image such that the resultant image provides better discrimination. We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a generative approach for classifying the probe image. An important feature of our algorithm is that it can handle resolution changes along with illumination variations. The effectiveness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset. It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms.
{"title":"Synthesis-based recognition of low resolution faces","authors":"Sumit Shekhar, Vishal M. Patel, R. Chellappa","doi":"10.1109/IJCB.2011.6117545","DOIUrl":"https://doi.org/10.1109/IJCB.2011.6117545","url":null,"abstract":"Recognition of low resolution face images is a challenging problem in many practical face recognition systems. Methods have been proposed in the face recognition literature for the problem when the probe is of low resolution, and a high resolution gallery is available for recognition. These methods modify the probe image such that the resultant image provides better discrimination. We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a generative approach for classifying the probe image. An important feature of our algorithm is that it can handle resolution changes along with illumination variations. The effectiveness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset. It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms.","PeriodicalId":103913,"journal":{"name":"2011 International Joint Conference on Biometrics (IJCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130614226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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