Face identification method based on multiscale local phase quantization characteristics

Abstract

The invention discloses a face identification method based on multiscale local phase quantization characteristics. The face identification method is realized under the condition that a digital camera serves as a sensor and under the support of a digital signal processing chip. The face identification method is characterized by including the steps as follows: conducting pretreatment such as alignment, scaling, filtering and the like on a face image obtained by the digital camera; calculating a horizontal gradient image and a vertical gradient image; calculating an integral image to obtain an image set of multiscale local phase quantization characteristics of a face; preliminarily obtaining a candidate characteristic set by applying Adaboost characteristic selector; and obtaining a face characteristic vector in low dimensional space by utilizing multiple subspace linear discrimination analyzers, and matching the face characteristic vector with a pre-built face characteristic template to obtain the identity information of identify people. The face identification method has good identification rate, low misclassification rate and fast calculation speed, is especially suitable for embedded products and can be popularized and applied in a large scale.

Description

technical field [0001] The invention belongs to a video recognition method, in particular to a video recognition method applied to face recognition. Background technique [0002] Face recognition technology is one of the biometric technologies that are currently being vigorously developed. The face recognition system mainly includes data acquisition subsystem, face detection subsystem and face recognition subsystem. Face feature extraction is the most critical technology of the face recognition subsystem. A good face feature extraction technology will make the extracted face feature value smaller and better in discrimination performance, which can improve the recognition rate and reduce the false recognition rate. The existing face feature extraction methods mainly include: based on geometric features, based on subspace analysis, based on wavelet theory, based on neural network, based on hidden Markov model, based on support vector machine and based on 3D model. method. T...

AI Technical Summary

Problems solved by technology

The idea of ​​the method based on geometric features is to extract the relative position and relative size of the representative parts of the face (such as eyebrows, eyes, nose, mouth, etc.) Easily affected by factors such as light, expression, occlusion, etc., the stability is not high

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