Total posture face identification method based on complete binary posture affinity scale invariant features

Abstract

Disclosed is a total posture face identification method based on complete binary posture affinity scale invariant features. By the method, identification of faces with total posture variations (from horizontal 90-degree left turning to horizontal 90-degree right turning, head raising and head lowering) can be achieved. Experiments analysis shows that affinity scale invariant features are invariant to faces turning in a plane and invariant to faces turning within 25 degrees in a plane, and accordingly the method acquires multiple face images of specific postures, extracts affinity scale invariant features of the face images, and combines the features to generate complete binary posture affinity scale invariant features so as to realize invariance to total posture face turning. The method needs no simulation training, so that the method is much better than methods based on models in terms of computation complexity. Meanwhile, for conditions with large posture variations, the identification performance is much better than traditional methods based on features. The method has certain application value and significance.

Description

technical field

[0001] The invention relates to gesture face recognition technology, in particular to the research and realization of a full-pose face recognition method based on complete binary pose affine scale-invariant features. Background technique

[0002] With the 911 terrorist attacks in the United States and the leakage of network CSDN user information, biometric recognition technology has attracted everyone's attention, and face biometric identification and authentication technology has always been a hot spot in the field of biometric recognition. However, in practical applications, face recognition is often affected by many factors. When the face posture changes, the expression changes, the external light changes, and the face is blocked (wearing a scarf) , sunglasses), etc., the performance of face recognition will drop a lot, which restricts the practical application of face recognition. Among them, the impact of face pose changes on face recognition is that th...

Examples

Embodiment Construction

[0028] The overall process of the technical solution of the present invention is as follows in the description attached figure 1 As shown, it is divided into template feature extraction stage and recognition stage. The technical scheme is tested on the CMUPIE face database, and the experimental results are attached figure 2 As shown, our method outperforms other existing methods, with an average recognition rate of 95.89%.

[0029] A. In the template feature extraction stage, for each person, collect the face image of its specific posture. The specific posture includes: horizontal rotation of 90 degrees to the left, horizontal rotation of 45 degrees to the left, horizontal rotation of 0 degrees, horizontal rotation of 45 degrees to the right, Rotate 90 degrees to the right horizontally, as attached image 3 Then, feature extraction is performed on each face image, and the obtained features are fused to obtain the final template feature, that is, the complete binary pose aff...

technical field

[0001] The invention relates to gesture face recognition technology, in particular to the research and realization of a full-pose face recognition method based on complete binary pose affine scale-invariant features. Background technique

[0002] With the 911 terrorist attacks in the United States and the leakage of network CSDN user information, biometric recognition technology has attracted everyone's attention, and face biometric identification and authentication technology has always been a hot spot in the field of biometric recognition. However, in practical applications, face recognition is often affected by many factors. When the face posture changes, the expression changes, the external light changes, and the face is blocked (wearing a scarf) , sunglasses), etc., the performance of face recognition will drop a lot, which restricts the practical application of face recognition. Among them, the impact of face pose changes on face recognition is that th...

Examples

Embodiment Construction

[0028] The overall process of the technical solution of the present invention is as follows in the description attached figure 1 As shown, it is divided into template feature extraction stage and recognition stage. The technical scheme is tested on the CMUPIE face database, and the experimental results are attached figure 2 As shown, our method outperforms other existing methods, with an average recognition rate of 95.89%.

[0029] A. In the template feature extraction stage, for each person, collect the face image of its specific posture. The specific posture includes: horizontal rotation of 90 degrees to the left, horizontal rotation of 45 degrees to the left, horizontal rotation of 0 degrees, horizontal rotation of 45 degrees to the right, Rotate 90 degrees to the right horizontally, as attached image 3 Then, feature extraction is performed on each face image, and the obtained features are fused to obtain the final template feature, that is, the complete binary pose aff...

Images