Face Gender Recognition based on Face Recognition Feature Vectors

Automatic facial gender recognition is a widely used task in the field of computer vision, which is very easy for a human, but very challenging for computers. In this paper, a face gender classification algorithm based on face recognition feature vectors is proposed. Firstly, face detection and preprocessing are performed on the input images, and the faces are adjusted to a unified format. Secondly, the face recognition model is used to extract feature vectors as the representation of the face in the feature space. Finally, machine learning methods are used to classify the extracted feature vector. Meanwhile, this study uses t-distributed Stochastic Neighbor Embedding (T-SNE) to visualize the face recognition feature vectors to verify the effectiveness of the face recognition feature vectors on the issue of gender classification. The proposed method has achieved a recognition rate of 99.2% and 98.7% on the FEI dataset and the SCIEN dataset, respectively. Besides, it also achieves a recognition rate of 97.4% on the Asian star face dataset, outperforming existing methods, which shows that the proposed method is helpful for the research of facial gender.