Pub Date : 2013-04-16DOI: 10.1109/CIBIM.2013.6607911
Joshua Adams, Henry Williams, J. Carter, G. Dozier
Author identification is the process of recognizing an author based on a sample of text. Feature selection is the process of selecting the most salient features required for recognition. In many cases, this results in an increase in recognition accuracy. In this paper, we apply Genetic and Evolutionary Feature Selection with Machine Learning (GEFeSML) to author identification. We then introduce Genetic Heuristic Development (GHD), a process to improve the matching process. GHD uses subsets of features found by GEFeSML to create a high performing heuristic for feature selection. This technique successfully increases recognition accuracy while significantly reducing the number of features required for recognition.
{"title":"Genetic Heuristic Development: Feature selection for author identification","authors":"Joshua Adams, Henry Williams, J. Carter, G. Dozier","doi":"10.1109/CIBIM.2013.6607911","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607911","url":null,"abstract":"Author identification is the process of recognizing an author based on a sample of text. Feature selection is the process of selecting the most salient features required for recognition. In many cases, this results in an increase in recognition accuracy. In this paper, we apply Genetic and Evolutionary Feature Selection with Machine Learning (GEFeSML) to author identification. We then introduce Genetic Heuristic Development (GHD), a process to improve the matching process. GHD uses subsets of features found by GEFeSML to create a high performing heuristic for feature selection. This technique successfully increases recognition accuracy while significantly reducing the number of features required for recognition.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"52 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134093358","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607906
M. S. Kumar, D. Nedumaran
Image enhancement is one of the pre-processing steps of fingerprint image processing, in which an image can be viewed with clear ridge and valley patterns. This paper presents a novel image enhancement method using Modified Histogram Equalization (MHE) based on the Adaptive Inverse Hyperbolic Tangent (AIHT) method. The algorithm was developed in the Texas Instruments CCS environment and implemented on the TMS320C6416 DSK. The proposed algorithm was tested in many high, low, and under contrast fingerprint images and the results obtained was found to have a better visual perception suitable for human interpretation or for implementation in the automated fingerprint identification system (AFIS).
{"title":"Development of image enhancement algorithm for fingerprint images in TMS320C6416 DSK","authors":"M. S. Kumar, D. Nedumaran","doi":"10.1109/CIBIM.2013.6607906","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607906","url":null,"abstract":"Image enhancement is one of the pre-processing steps of fingerprint image processing, in which an image can be viewed with clear ridge and valley patterns. This paper presents a novel image enhancement method using Modified Histogram Equalization (MHE) based on the Adaptive Inverse Hyperbolic Tangent (AIHT) method. The algorithm was developed in the Texas Instruments CCS environment and implemented on the TMS320C6416 DSK. The proposed algorithm was tested in many high, low, and under contrast fingerprint images and the results obtained was found to have a better visual perception suitable for human interpretation or for implementation in the automated fingerprint identification system (AFIS).","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"29 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115407178","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607916
K. Karande
In this paper we have proposed a unique approach for face recognition based on modular Independent Component Analysis (ICA) with local facial features. The face images are segmented based on skin color using YCbCr color space. In this research work we have considered the samples of individual person which consist of sufficient number of images having pose variations, facial expressions and changes in illumination from Asian face database. The proposed method is based on local facial feature extraction after face segmentation. The local components such as eyes, nose, mouth (lips) are extracted automatically. These local components are used to obtain independent components. Using the independent components of these local facial components, the face recognition task is performed by ICA algorithms.
{"title":"Localized spatiotemporal modular ICA for face recognition","authors":"K. Karande","doi":"10.1109/CIBIM.2013.6607916","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607916","url":null,"abstract":"In this paper we have proposed a unique approach for face recognition based on modular Independent Component Analysis (ICA) with local facial features. The face images are segmented based on skin color using YCbCr color space. In this research work we have considered the samples of individual person which consist of sufficient number of images having pose variations, facial expressions and changes in illumination from Asian face database. The proposed method is based on local facial feature extraction after face segmentation. The local components such as eyes, nose, mouth (lips) are extracted automatically. These local components are used to obtain independent components. Using the independent components of these local facial components, the face recognition task is performed by ICA algorithms.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126899994","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607914
Seyed Mohammad Hassan Anvar, W. Yau, K. Nandakumar, E. Teoh
Classical face detection algorithm works on only near frontal faces. Extending it to other poses and in-plane rotated faces require separately trained classifiers which increases both the training and processing time. We solve this instead by developing a reference model that is capable of detecting upright faces in various poses. Then a probabilistic framework is used to estimate occurrence of in-plane rotated faces. Experimental results showed that the proposed approach can achieve face detection accuracy comparable to state-of-the-art approaches but returns more accurate in-plane rotation angle estimation and is much faster. Unlike other approaches, the proposed method is easy to train, requiring only a small number of images and only one manually labeled face image.
{"title":"Estimating in-plane rotation angle for face images from multi-poses","authors":"Seyed Mohammad Hassan Anvar, W. Yau, K. Nandakumar, E. Teoh","doi":"10.1109/CIBIM.2013.6607914","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607914","url":null,"abstract":"Classical face detection algorithm works on only near frontal faces. Extending it to other poses and in-plane rotated faces require separately trained classifiers which increases both the training and processing time. We solve this instead by developing a reference model that is capable of detecting upright faces in various poses. Then a probabilistic framework is used to estimate occurrence of in-plane rotated faces. Experimental results showed that the proposed approach can achieve face detection accuracy comparable to state-of-the-art approaches but returns more accurate in-plane rotation angle estimation and is much faster. Unlike other approaches, the proposed method is easy to train, requiring only a small number of images and only one manually labeled face image.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128941951","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607909
R. D. Labati, A. Genovese, V. Piuri, F. Scotti
Contactless fingerprint recognition systems are being researched in order to reduce intrinsic limitations of traditional biometric acquisition technologies, encompassing the release of latent fingerprints on the sensor platen, non-linear spatial distortions in the captured samples, and relevant image differences with respect to the moisture level and pressure of the fingertip on the sensor surface.Fingerprint images captured by single cameras, however, can be affected by perspective distortions and deformations due to incorrect alignments of the finger with respect to the camera optical axis. These non-idealities can modify the ridge pattern and reduce the visibility of the fingerprint details, thus decreasing the recognition accuracy. Some systems in the literature overcome this problem by computing three-dimensional models of the finger. Unfortunately, such approaches are usually based on complex and expensive acquisition setups, which limit their portability in consumer devices like mobile phones and tablets. In this paper, we present a novel approach able to recover perspective deformations and improper fingertip alignments in single camera systems. The approach estimates the orientation difference between two contactless fingerprint acquisitions by using neural networks, and permits to register the considered samples by applying the estimated rotation angle to a synthetic three-dimensional model of the finger surface. The generalization capability of neural networks offers a significant advantage by allowing processing a robust estimation of the orientation difference with a very limited need of computational resources with respect to traditional techniques. Experimental results show that the approach is feasible and can effectively enhance the recognition accuracy of single-camera biometric systems. On the evaluated dataset of 800 contactless images, the proposed method permitted to decrease the equal error rate of the used biometric system from 3.04% to 2.20%.
{"title":"Contactless fingerprint recognition: A neural approach for perspective and rotation effects reduction","authors":"R. D. Labati, A. Genovese, V. Piuri, F. Scotti","doi":"10.1109/CIBIM.2013.6607909","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607909","url":null,"abstract":"Contactless fingerprint recognition systems are being researched in order to reduce intrinsic limitations of traditional biometric acquisition technologies, encompassing the release of latent fingerprints on the sensor platen, non-linear spatial distortions in the captured samples, and relevant image differences with respect to the moisture level and pressure of the fingertip on the sensor surface.Fingerprint images captured by single cameras, however, can be affected by perspective distortions and deformations due to incorrect alignments of the finger with respect to the camera optical axis. These non-idealities can modify the ridge pattern and reduce the visibility of the fingerprint details, thus decreasing the recognition accuracy. Some systems in the literature overcome this problem by computing three-dimensional models of the finger. Unfortunately, such approaches are usually based on complex and expensive acquisition setups, which limit their portability in consumer devices like mobile phones and tablets. In this paper, we present a novel approach able to recover perspective deformations and improper fingertip alignments in single camera systems. The approach estimates the orientation difference between two contactless fingerprint acquisitions by using neural networks, and permits to register the considered samples by applying the estimated rotation angle to a synthetic three-dimensional model of the finger surface. The generalization capability of neural networks offers a significant advantage by allowing processing a robust estimation of the orientation difference with a very limited need of computational resources with respect to traditional techniques. Experimental results show that the approach is feasible and can effectively enhance the recognition accuracy of single-camera biometric systems. On the evaluated dataset of 800 contactless images, the proposed method permitted to decrease the equal error rate of the used biometric system from 3.04% to 2.20%.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133006770","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607908
G. Santos, Hugo Proença
The periocular region has recently emerged as a promising trait for unconstrained biometric recognition, specially on cases where neither the iris and a full facial picture can be obtained. Previous studies concluded that the regions in the vicinity of the human eye - the periocular region- have surprisingly high discriminating ability between individuals, are relatively permanent and easily acquired at large distances. Hence, growing attention has been paid to periocular recognition methods, on the performance levels they are able to achieve, and on the correlation of the responses given by other. This work overviews the most relevant research works in the scope of periocular recognition: summarizes the developed methods, and enumerates the current issues, providing a comparative overview. For contextualization, a brief overview of the biometric field is also given.
{"title":"Periocular biometrics: An emerging technology for unconstrained scenarios","authors":"G. Santos, Hugo Proença","doi":"10.1109/CIBIM.2013.6607908","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607908","url":null,"abstract":"The periocular region has recently emerged as a promising trait for unconstrained biometric recognition, specially on cases where neither the iris and a full facial picture can be obtained. Previous studies concluded that the regions in the vicinity of the human eye - the periocular region- have surprisingly high discriminating ability between individuals, are relatively permanent and easily acquired at large distances. Hence, growing attention has been paid to periocular recognition methods, on the performance levels they are able to achieve, and on the correlation of the responses given by other. This work overviews the most relevant research works in the scope of periocular recognition: summarizes the developed methods, and enumerates the current issues, providing a comparative overview. For contextualization, a brief overview of the biometric field is also given.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125119567","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607917
B. Oh, K. Toh
This paper proposes a technique for face feature extraction using sinusoidal projection. Essentially, the technique uses a projection matrix, which is formed by stacking vectors with sinusoidal values at different frequencies, to directly multiply with raw image matrix for weighted feature extraction. Orthogonality among vectors within the sinusoidal projection matrix is observed when the frequencies are chosen as multiples of the fundamental frequency. The proposed technique shows promising verification performance on three face databases.
{"title":"Face identity verification based on sinusoidal projection","authors":"B. Oh, K. Toh","doi":"10.1109/CIBIM.2013.6607917","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607917","url":null,"abstract":"This paper proposes a technique for face feature extraction using sinusoidal projection. Essentially, the technique uses a projection matrix, which is formed by stacking vectors with sinusoidal values at different frequencies, to directly multiply with raw image matrix for weighted feature extraction. Orthogonality among vectors within the sinusoidal projection matrix is observed when the frequencies are chosen as multiples of the fundamental frequency. The proposed technique shows promising verification performance on three face databases.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116343911","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607913
W. M. Isa, J. Abdullah, C. Eswaran
This paper presents a classification analysis of gait biometric on twins and non-twins siblings. The aim of this paper is to investigate the existence or inexistence of similarity in the gait of twins and compare it to the gait of non-twins siblings. The motivation behind this paper is that a video-based surveillance system may not be able to rely on face biometric alone when dealing with twins. The features used are the angular displacement walking trajectories of lower limbs. Also this paper proposes a gait cycle normalization task via Bezier polynomial root-finding and re-sampling to ensure a robust analysis against differences in walking speed. Two established classifiers, the linear discriminant analysis (LDA) and k-nearest neighbor are used to classify the data sets of twins and non-twins siblings. Results may indicate that there is similarity in the gait of twins.
{"title":"Gait classification of twins and non-twins siblings","authors":"W. M. Isa, J. Abdullah, C. Eswaran","doi":"10.1109/CIBIM.2013.6607913","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607913","url":null,"abstract":"This paper presents a classification analysis of gait biometric on twins and non-twins siblings. The aim of this paper is to investigate the existence or inexistence of similarity in the gait of twins and compare it to the gait of non-twins siblings. The motivation behind this paper is that a video-based surveillance system may not be able to rely on face biometric alone when dealing with twins. The features used are the angular displacement walking trajectories of lower limbs. Also this paper proposes a gait cycle normalization task via Bezier polynomial root-finding and re-sampling to ensure a robust analysis against differences in walking speed. Two established classifiers, the linear discriminant analysis (LDA) and k-nearest neighbor are used to classify the data sets of twins and non-twins siblings. Results may indicate that there is similarity in the gait of twins.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114084114","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607910
O. Zoidi, N. Nikolaidis, I. Pitas
In this paper, a method for performing semiautomatic identity label annotation on facial images, obtained from monocular and stereoscopic videos is introduced. The proposed method exploits prior information for the data structure, obtained from the application of a clustering algorithm, for the selection of the facial images from which label inference should begin. Then, a sparse graph is constructed according to the Linear Neighborhood Propagation (LNP) method and, finally, label inference is performed according to an iterative update rule. In the case of stereoscopic videos, the classification decision is determined by the combined information of the left and right channels. The objective of the proposed framework is to be used by archivists for semi-automatic annotation of television content, in order to further enable journalists to directly access video shots/frames of interest.
{"title":"Exploiting clustering and stereo information in label propagation on facial images","authors":"O. Zoidi, N. Nikolaidis, I. Pitas","doi":"10.1109/CIBIM.2013.6607910","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607910","url":null,"abstract":"In this paper, a method for performing semiautomatic identity label annotation on facial images, obtained from monocular and stereoscopic videos is introduced. The proposed method exploits prior information for the data structure, obtained from the application of a clustering algorithm, for the selection of the facial images from which label inference should begin. Then, a sparse graph is constructed according to the Linear Neighborhood Propagation (LNP) method and, finally, label inference is performed according to an iterative update rule. In the case of stereoscopic videos, the classification decision is determined by the combined information of the left and right channels. The objective of the proposed framework is to be used by archivists for semi-automatic annotation of television content, in order to further enable journalists to directly access video shots/frames of interest.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128204310","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 : 2013-04-16DOI: 10.1109/CIBIM.2013.6607918
Elisa Barroso, G. Santos, Hugo Proença
Facial expressions result from movements of muscular action units, in response to internal emotion states or perceptions, and it has been shown that they decrease the performance of face-based biometric recognition techniques. This paper focuses in the recognition of facial expressions and has the following purposes: 1) confirm the suitability of using dense image descriptors widely known in biometrics research (e.g., local binary patterns and histogram of oriented gradients) to recognize facial expressions; 2) compare the effectiveness attained when using different regions of the face to recognize expressions; 3) compare the effectiveness attained when the identity of subjects is known/unknown, before attempting to recognize their facial expressions.
{"title":"Facial expressions: Discriminability of facial regions and relationship to biometrics recognition","authors":"Elisa Barroso, G. Santos, Hugo Proença","doi":"10.1109/CIBIM.2013.6607918","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607918","url":null,"abstract":"Facial expressions result from movements of muscular action units, in response to internal emotion states or perceptions, and it has been shown that they decrease the performance of face-based biometric recognition techniques. This paper focuses in the recognition of facial expressions and has the following purposes: 1) confirm the suitability of using dense image descriptors widely known in biometrics research (e.g., local binary patterns and histogram of oriented gradients) to recognize facial expressions; 2) compare the effectiveness attained when using different regions of the face to recognize expressions; 3) compare the effectiveness attained when the identity of subjects is known/unknown, before attempting to recognize their facial expressions.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133110743","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：