Fuzzy similarity-based classification method for gender recognition using 3D facial images

In this paper, we propose a new fuzzy similarity-based classification (FSBC) method for the task of gender recognition. The proposed method characterises each individual by extracting geometrical features from a 3D facial image using pertinent radial curves. Our approach includes representing the extracted features using fuzzy sets to handle imprecision in its values. Also, the proposed FSBC method recognises the gender of a new person by evaluating his similarity to the male and female samples pre-set as gender representatives set, then we aggregate the obtained similarities to compute the scores of belonging to each gender. In the end, we ascribe to each new person the gender with the higher score. With the proposed method, two main advantages are obtained: First, we used the OWA operator and RIM quantifier to define the percentage of significant features for the similarity assessment. Second, the aggregation process was performed using compensatory operators to ensure the selected gender has high similarities. Experiments were conducted using FRAV3D database, by considering only one frontal pose in the gender representatives set. The obtained gender recognition rate of the proposed method was very promising compared to other classification method.