Structured learning and prediction in face sketch gender classification and recognition
Khalid Ounachad, Mohamed Oualla, Abdlghani Souhar, Abdelalim Sadiq · International Journal of Computational Vision and Robotics · 2020
Structured prediction methods have become an attractive tool for many machine-learning applications. For this reason, the objective of this paper is to identify the gender of the human being by using their face sketch applying a structured learning approach. We used a deep geometric descriptor as features and the gender as labels, and structured learning and prediction approach as matching. The basic idea is to extract perfect face ratios for the face sketch as a feature and the labels are the gender. To extract perfect face ratios, we use the landmarks point in the face then sixteen features will be extract. The training and the testing tasks are applied to CUHK face sketch dataset (CUFS). An experimental evaluation demonstrates the satisfactory performance of our approach on CUFS and the recognition rate reaches more than 98%.