Abstract
Facial age classification has become relevant to an increasing amount of applications, particularly with the rise of social platforms and social media, facial age estimation becomes more essential. In this paper a new age classification method based on deep convolution neural network(DCNN) is proposed, the DCNN is used to extract facial age features, then the Maximum joint probability classifier based on collaborative representation (MJPCCR) is used to classify them. Three data sets are used to validate our approach. We also send the features extracted from the DCNN model to the SVM classifier, then compare the results with those obtained by our method. And we also send the age features output from the activations of the last layer into the MJPCCR and the SVM classifier separately, and compare their results. Experiments show that the performance of our method is superior to that of the previous methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
