Face image gender recognition system based on stack type sparse self-coding

Abstract

The invention relates to a face image gender recognition method based on stack type sparse self-coding, and belongs to the field of image recognition, machine learning, and computer vision. A training process of the method includes image graying, histogram equalization, geometric correction, image normalization, the training of a sparse self-coding model, logic regression classifier training, a fine tuning model, and model fusion of face standard databases FERET and CAS-PEAL-R1, and a prediction process comprises the capturing of natural scene images by a camera, image graying, histogram equalization, face detection, geometric correction, image normalization, the prediction by employing a stack type sparse self-coding model, and result marking. According to the method, the problem of face gender recognition is solved by employing the stack type sparse self-coding model, combination characteristics of the images can be learned layer by layer, original signals can be better represented in an abstract manner, characteristics extracted by a hiding unit are further adjusted by the adoption of fine tuning, and the recognition accuracy is higher.

Description

technical field [0001] The invention relates to a face image gender recognition method, in particular to a face gender recognition system, which belongs to the fields of image recognition, machine learning and computer vision. Background technique [0002] With the development of computer science and technology, computers are developing from computing to intelligent machines, and digitization, networking and intelligence have become the development direction of the information field. On this basis, biometric identification technology has been developed rapidly. Commonly used biometric features include: face, fingerprint, hand type, iris, voice, etc. [0003] Human face is one of the most important biological characteristics of human beings, which plays a major role in identifying identity and conveying emotions. Face images contain a lot of information, such as identity, gender, age, race, expression, etc. It has become possible to estimate the characteristics of a person...

AI Technical Summary

Problems solved by technology

In addition, the classifiers of gender recognition work done by predecessors are mainly shallow models (generally, the number of hidden layer nodes is less than or equal to 2, which becomes a shallow model). There is no feature learning process, and the combined features cannot be learned, resulting in a low recognition rate. Lifting becomes the bottleneck again

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