Unraveling Human Perception of Facial Aging Using Eye Gaze

Age classification from facial images

Abstract: The ability to classify age from a facial image has not been pursued in computer vision. This research addresses the limited task of age classification of a facial image into a baby, young adult, and senior adult. This is the first reported work to classify age, and to successfully extract and use natural wrinkles. We present a theory and practical computations for visual age classification from facial images, based on cranio-facial changes in feature-position ratios, and on skin wrinkle analysis. Three age groups are classified. >

Two Fixations Suffice in Face Recognition

Abstract: It is well known that there exist preferred landing positions for eye fixations in visual word recognition. However, the existence of preferred landing positions in face recognition is less well established. It is also unknown how many fixations are required to recognize a face. To investigate these questions, we recorded eye movements during face recognition. During an otherwise standard face-recognition task, subjects were allowed a variable number of fixations before the stimulus was masked. We found that optimal recognition performance is achieved with two fixations; performance does not improve with additional fixations. The distribution of the first fixation is just to the left of the center of the nose, and that of the second fixation is around the center of the nose. Thus, these appear to be the preferred landing positions for face recognition. Furthermore, the fixations made during face learning differ in location from those made during face recognition and are also more variable in duration; thi...

Age estimation from face images: Human vs. machine performance

Abstract: There has been a growing interest in automatic age estimation from facial images due to a variety of potential applications in law enforcement, security control, and human-computer interaction. However, despite advances in automatic age estimation, it remains a challenging problem. This is because the face aging process is determined not only by intrinsic factors, e.g. genetic factors, but also by extrinsic factors, e.g. lifestyle, expression, and environment. As a result, different people with the same age can have quite different appearances due to different rates of facial aging. We propose a hierarchical approach for automatic age estimation, and provide an analysis of how aging influences individual facial components. Experimental results on the FG-NET, MORPH Album2, and PCSO databases show that eyes and nose are more informative than the other facial components in automatic age estimation. We also study the ability of humans to estimate age using data collected via crowdsourcing, and show that the cumulative score (CS) within 5-year mean absolute error (MAE) of our method is better than the age estimates provided by humans.

Looking just below the eyes is optimal across face recognition tasks

Abstract: When viewing a human face, people often look toward the eyes. Maintaining good eye contact carries significant social value and allows for the extraction of information about gaze direction. When identifying faces, humans also look toward the eyes, but it is unclear whether this behavior is solely a byproduct of the socially important eye movement behavior or whether it has functional importance in basic perceptual tasks. Here, we propose that gaze behavior while determining a person’s identity, emotional state, or gender can be explained as an adaptive brain strategy to learn eye movement plans that optimize performance in these evolutionarily important perceptual tasks. We show that humans move their eyes to locations that maximize perceptual performance determining the identity, gender, and emotional state of a face. These optimal fixation points, which differ moderately across tasks, are predicted correctly by a Bayesian ideal observer that integrates information optimally across the face but is constrained by the decrease in resolution and sensitivity from the fovea toward the visual periphery (foveated ideal observer). Neither a model that disregards the foveated nature of the visual system and makes fixations on the local region with maximal information, nor a model that makes center-of-gravity fixations correctly predict human eye movements. Extension of the foveated ideal observer framework to a large database of real-world faces shows that the optimality of these strategies generalizes across the population. These results suggest that the human visual system optimizes face recognition performance through guidance of eye movements not only toward but, more precisely, just below the eyes.

Plastic Surgery: A New Dimension to Face Recognition

Abstract: Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem.