Kwon and Lobo are among the first to formulate an age estimation approach based on the facial image. The problem of perceived facial age has also been studied by computer vision researchers. a source of information is missing, the ability to estimate age is not completely diminished. The experiments conducted in, conclude that even if a region is hidden in the face image, i.e. In an interesting experiment, George and Hole observed that manipulations in the features influence the age estimation precision. The findings suggest that the eye region is important for age prediction. Jones and Smith analyzed the influence of local features such as eyes and nose on age estimation. The study suggests that the predicted age deviated by 2.39 years. Burt and Perrett evaluated the accuracy of young and old adults in estimating the age of subjects ranging from 20 to 54 years. The researchers have examined how adept humans are in estimating the facial age and various aspects that could affect the perceived age. However, the proficiency may vary depending on both local and global features. In an extensive literature review on age estimation by humans, Rhodes had shown that humans can estimate the age of previously unseen faces quite accurately. Accurate age estimation is crucial in a variety of situations such as the need to automatically estimate the age of an individual buying alcohol or cigarettes.

There are two aspects of building an age-invariant face recognition system: (1) facial age estimation and (2) age-separated face recognition. Face images of an individual illustrating variations due to aging across different years. 1 shows face images of an individual with age variations.įigure 1. During formative years of a person, the variations in the shape of a face are more prominent while in the later stages of life, texture variations such as wrinkles and pigmentation are more visible. Further, the process of facial aging is not uniform across time. It has been observed that every person has a personalized aging pattern depending on numerous factors such as genetics, ethnicity, dietary habits, environmental conditions, and stress level. In fact, for large-scale applications, adding invariance to aging is a very important requirement.Īging affects the appearance of a face in diverse ways. Developing age-invariant face recognition algorithms can prove to be beneficial in many applications such as locating missing persons, homeland security, and passport services. Another important challenge of face recognition is matching face images with age variations. However, it is crucial as well as challenging to develop an algorithm which is robust to variations such as pose, illumination, and expression. Over the past few decades, many automatic face recognition algorithms have been developed. Use of facial information during these communications is made possible by the remarkable ability of humans to accurately recognize and interpret faces in real time. This knowledge plays a significant role during face-to-face communication between humans. 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.įacial images convey a substantial amount of information such as the individual's identity, ethnicity, gender, age, and emotional state. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. 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. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. Age estimation involves predicting the age of an individual given his/her facial image.

Such a study has two components - facial age estimation and age-separated face recognition. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. It is interesting to observe how humans perceive facial age.

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals.

Face morph age progression applications