Author(s)

Jing Ren

Corresponding Author

Jing Ren

ABSTRACT

In order to improve the accuracy and anti-interference of face recognition, a face recognition system based on Hadoop cloud computing environment is proposed. Facial corner feature detection technique is used to sample facial image feature of moving face, and face image position and pose adjustment and grayscale feature matching are applied to the collected face image. Wavelet feature decomposition method is used to reduce the interference of noise to dynamic face feature extraction, and adaptive feature separation method is used to detect the edge contour of face and segment the face image in gray face image. The feature quantity which reflects the difference information of mobile face is extracted, and the corner location is used to locate and recognize the facial features. All the key points of facial features are extracted from the active contour area, and the face recognition algorithm is improved. Facial feature extraction method is used to extract spectral density feature of Hadoop cloud computing environment. Based on face image information recognition technology, face image information collection design of face recognition image, face image information integration processing and cross-compilation control under VIX bus protocol are realized, the face recognition system software development and design are obtained. The software test results show that the face recognition system has better real-time performance, the face detection accuracy is improved, it has better reliability and better running intelligence performance.

KEYWORDS

Hadoop, Cloud Computing Environment, Face Recognition, Feature Extraction