The present study investigated the role of differential experience in one’s processing of facial age information. Study 1 examined how differential experience with own- and other-race individuals, as well as differential experience with own- and other-age individuals, influences children’s and adults’ abilities to process facial age information. Study 2 examined how differential sociocultural experiences influence adults’ abilities to process facial age information. The results suggest that the influence of differential experience with own- and other-race faces is most evident when individuals have extremely limited to no experience with other-race faces. There was also a clear other-age effect in young adults’ facial age judgments, presumably due to their extensive experience with own-age peers. However 9- to 10-year-olds and 13- to 14-year-olds also showed an advantage in processing facial age information for young adult faces relative to child and middle-age adult faces. Thus, the 9- to 10-year-olds and 13- to 14-year-olds may have also had the most extensive experience with young adult individuals relative to individuals from other age groups. In addition, results suggest that the efficiency with which individuals process facial age information is influenced by differential sociocultural emphases on the need to differentiate between the facial ages of social partners.

The present study investigated the role of differential experience in one’s processing of facial age information. Study 1 examined how differential experience with own- and other-race individuals, as well as differential experience with own- and other-age individuals, influences children’s and adults’ abilities to process facial age information. Study 2 examined how differential sociocultural experiences influence adults’ abilities to process facial age information. The results suggest that the influence of differential experience with own- and other-race faces is most evident when individuals have extremely limited to no experience with other-race faces. There was also a clear other-age effect in young adults’ facial age judgments, presumably due to their extensive experience with own-age peers. However 9- to 10-year-olds and 13- to 14-year-olds also showed an advantage in processing facial age information for young adult faces relative to child and middle-age adult faces. Thus, the 9- to 10-year-olds and 13- to 14-year-olds may have also had the most extensive experience with young adult individuals relative to individuals from other age groups. In addition, results suggest that the efficiency with which individuals process facial age information is influenced by differential sociocultural emphases on the need to differentiate between the facial ages of social partners.

The perception of human faces is affected by different facial features. For example, older faces are processed differently to younger ones and faces expressing diverse emotions are also processed differently. Research shows that angry faces are more attended to compared to neutral or other expressive faces, which is known as the ‘threat advantage’. This is evidenced by research on the late positive potential (LPP). The LPP is an event-related potential (ERP) component associated with affective processing, which seems to strongly respond to threats. The literature has indicated that older faces can elicit larger LPPs compared to young and neutral faces, and the LPP is more sensitive to emotional faces. The current experiment investigated subjective ratings in addition to the LPP in response to neutral and angry faces of young and old individuals, to examine how facial age influences the perception of anger. In a facial rating task, both the young and the old angry faces were rated as threatening faces, while old neutral faces were indicated to be more threatening than young neutral faces. Similarly, participants had a higher LPP for old angry faces. This data, in combination, suggests a higher emotional salience of old angry faces compared to either young angry or (young or old) neutral faces.

Yes / In recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side-view of face images. Here we exploit this by using a pre-trained deep residual neural network (ResNet) to extract features. We then utilize a sparse partial least squares regression approach to estimate ages. Despite having less information as compared to frontal images, our results show that the extracted deep features achieve a promising performance.