Yadav Daksha, Singh Richa, Vatsa Mayank, Noore Afzel
West Virginia University, Morgantown, West Virginia, United States of America.
IIIT Delhi, New Delhi, Delhi, India.
PLoS One. 2014 Dec 4;9(12):e112234. doi: 10.1371/journal.pone.0112234. eCollection 2014.
Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.
人类利用面部外观、性别、表情、衰老模式及其他辅助信息来识别个体。观察人类如何感知面部年龄很有意思。分析这些属性有助于理解面部衰老现象，而将研究结果纳入考量则有助于设计有效的算法。这样的研究有两个部分——面部年龄估计和按年龄分类的人脸识别。年龄估计涉及根据一个人的面部图像预测其年龄。另一方面，按年龄分类的人脸识别包括根据一个人的按年龄分类的图像识别该个体。在本研究中，我们调查了人类利用哪些面部线索来估计不同年龄组人群的年龄，同时分析了一个人的性别、年龄和种族对年龄估计能力的影响。我们还分析了诸如双眼和嘴巴区域等不同面部区域如何影响年龄估计和识别能力。最后，我们提出了一种年龄不变人脸识别算法，该算法融入了从这些观察中获得的知识。我们研究的主要观察结果如下：（1）新生儿和幼儿的年龄组最容易估计；（2）性别和种族不影响年龄组估计的判断；（3）面部作为一个全局特征，对于在按年龄分类的人脸识别中取得良好性能至关重要；（4）与现有算法相比，所提出的算法在识别性能上有所提高，并且在以年轻图像作为探测场景时也优于一个商业系统。
