Automatic gender recognition for “in the wild” facial images using convolutional neural networks

Automatic recognition of human demographical attributes has implications in a variety of domains, such as surveillance systems, human computer interaction, marketing etc. In this paper, we present an automatic gender recognition method from facial images based on convolutional neural networks. In order to train the network, we merged together several face databases and also gathered and annotated a ∼70000 facial images from the internet. We trained, evaluated and compared several network architectures that achieved impressive results on other computer vision tasks. The best accuracy is obtained using Inception-v4 network: 98.2% on our dataset, and 84% on Adience dataset.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Lei Chen,et al.  Gender Classification Based on Fusion of Weighted Multi-View Gait Component Distance , 2009, 2009 Chinese Conference on Pattern Recognition.

[3]  Tal Hassner,et al.  Age and Gender Estimation of Unfiltered Faces , 2014, IEEE Transactions on Information Forensics and Security.

[4]  Joshua Correll,et al.  The Chicago face database: A free stimulus set of faces and norming data , 2015, Behavior research methods.

[5]  Denise C. Park,et al.  A lifespan database of adult facial stimuli , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[6]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[7]  Wei Gao,et al.  Face Gender Classification on Consumer Images in a Multiethnic Environment , 2009, ICB.

[8]  C. Thomaz,et al.  A new ranking method for principal components analysis and its application to face image analysis , 2010, Image Vis. Comput..

[9]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[10]  Caifeng Shan,et al.  Learning local binary patterns for gender classification on real-world face images , 2012, Pattern Recognit. Lett..

[11]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Bok-Min Goi,et al.  Recognizing Human Gender in Computer Vision: A Survey , 2012, PRICAI.

[13]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[15]  Yun Fu,et al.  Gender recognition from body , 2008, ACM Multimedia.