A Statistical Assessment of Subject Factors in the PCA Recognition of Human Faces

G. Givens, Ross Beveridge, B. Draper, D. Bolme
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引用次数: 83

Abstract

Some people's faces are easier to recognize than others, but it is not obvious what subject-specific factors make individual faces easy or difficult to recognize. This study considers 11 factors that might make recognition easy or difficult for 1,072 human subjects in the FERET dataset. The specific factors are: race (white, Asian, African-American, or other), gender, age (young or old), glasses (present or absent), facial hair (present or absent), bangs (present or absent), mouth (closed or other), eyes (open or other), complexion (clear or other), makeup (present or absent), and expression (neutral or other). An ANOVA is used to determine the relationship between these subject covariates and the distance between pairs of images of the same subject in a standard Eigenfaces subspace. Some results are not terribly surprising. For example, the distance between pairs of images of the same subject increases for people who change their appearance, e.g., open and close their eyes, open and close their mouth or change expression. Thus changing appearance makes recognition harder. Other findings are surprising. Distance between pairs of images for subjects decreases for people who consistently wear glasses, so wearing glasses makes subjects more recognizable. Pairwise distance also decreases for people who are either Asian or African-American rather than white. A possible shortcoming of our analysis is that minority classifications such as African-Americans and wearers-of-glasses are underrepresented in training. Followup experiments with balanced training addresses this concern and corroborates the original findings. Another possible shortcoming of this analysis is the novel use of pairwise distance between images of a single person as the predictor of recognition difficulty. A separate experiment confirms that larger distances between pairs of subject images implies a larger recognition rank for that same pair of images, thus confirming that the subject is harder to recognize.
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人脸主成分分析中主体因素的统计分析
有些人的脸比其他人的脸更容易识别,但不清楚是什么特定于受试者的因素使个人的脸容易或难以识别。这项研究考虑了11个因素,这些因素可能会使FERET数据集中的1072名人类受试者的识别变得容易或困难。具体因素有:种族(白人、亚洲人、非裔美国人或其他)、性别、年龄(年轻或年老)、眼镜(戴或不戴)、面部毛发(戴或不留)、刘海(留或不留)、嘴巴(闭或其他)、眼睛(睁开或其他)、肤色(清澈或其他)、妆容(戴或不戴)和表情(中性或其他)。方差分析用于确定这些主体协变量与标准特征面子空间中同一主体的图像对之间的距离之间的关系。有些结果并不特别令人惊讶。例如,对于改变外表的人来说,例如,睁眼和闭眼、张嘴和闭嘴或改变表情,同一对象的成对图像之间的距离会增加。因此,改变外表会增加识别难度。其他发现令人惊讶。对于经常戴眼镜的人来说,被试的图像之间的距离会缩短,所以戴眼镜会让被试更容易被识别。与白人相比,亚裔或非裔美国人的两两距离也会减少。我们分析的一个可能的缺点是,像非洲裔美国人和戴眼镜的人这样的少数族裔在培训中的代表性不足。平衡训练的后续实验解决了这一问题,并证实了最初的发现。这种分析的另一个可能的缺点是,它新颖地使用了单个人的图像之间的成对距离作为识别难度的预测因子。一项单独的实验证实,两组被试图像之间的距离越大,意味着同一对图像的识别等级越高,从而证实了被试更难被识别。
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