Using Machine Learning to Judge Sexual Faithfulness Based on Human Perception of Face

Document Type

Conference Proceeding

Publication Date



People will analyze the faithfulness of others through facial features and expressions when they come into contact with strangers. In mating choice, the accurate estimation of sexual unfaithfulness will reduce the reproductive cost of an unfaithful mate. Therefore, this paper uses machine learning to judge sexual faithfulness based on human perception of the face in four dimensions: attractiveness, trustworthiness, faithfulness, and sexual dimorphism. First of all, this study uses three conventional machine learning algorithms to classify the data. Then we use soft voting to build an ensemble model. The study reveals that women have better performance than men at judging sexual faithfulness. Moreover, the AUC of our ensemble model is 0.861 based on female and 0.789 based on male.

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering



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