Gender Classification by Fuzzy Inference System

Gender classification from face images has many applications and is thus an important research topic. This paper presents an approach to gender classification based on shape and texture information gathered to design a fuzzy decision making system. Beside face shape features, Zernik moments are applied as system inputs to improve the system output which is considered as the probability of being male face image. After parameters tuning of the proposed fuzzy decision making system, 85.05% classification rate on the FERET face database (including 1199 individuals from different poses and facial expressions) shows acceptable results compare to other methods.

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