Caffe model for gender classification and deploy prototext.Caffe model for age classification and deploy prototext.We now provide a git repository to help reproduce our results.If you find our code useful, please add suitable reference to our paper in your work. We provide the convolutional neural network models for age and gender classification used in the paper. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. AbstractĪutomatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Most notably, extreme blur (low-resolution), occlusions, out-of-plane pose variations, expressions and more. These images represent some of the challenges of age and gender estimation from real-world, unconstrained images. on Computer Vision and Pattern Recognition (CVPR), Boston, 2015.įaces from the Adience benchmark for age and gender classification. IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf. Age and Gender Classification Using Convolutional Neural Networks. Recommended citation: Gil Levi and Tal Hassner. on Computer Vision and Pattern Recognition (CVPR), Boston, 2015 Published in IEEE Workshop on Analysis and Modeling of Faces and Gestures (AMFG), at the IEEE Conf.

Age and Gender Classification Using Convolutional Neural Networks

Taal movie analysis