Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Understanding critical factors in appearance-based gender categorization
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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Automated gender recognition has become an interesting and challenging research problem in recent years with its potential applications in security industry and human-computer interaction systems. In this paper we present a novel feature representation, namely Interlaced Derivative Patterns (IDP), which is a derivative-based technique to extract discriminative facial features for gender classification. The proposed technique operates on a neighborhood around a pixel and concatenates the extracted regional feature distributions to form a feature vector. The experimental results demonstrate the effectiveness of the IDP method for gender classification, showing that the proposed approach achieves 29.6% relative error reduction compared to Local Binary Patterns (LBP), while it performs over four times faster than Local Derivative Patterns (LDP).