Detecting faces in the wavelet compressed domain

摘要

A novel technique that can implement face detection directly in the wavelet compressed domain is presented in this paper. The algorithm takes the entropy decoding and inverse quantized wavelet transform coefficients of JPEG2000 picture as input, and outputs the locations of the detected faces. The main contribution of this work is in proposing a multi-level gradient energy representation of face pattern based on wavelet compressed data, which permits pertinent high contrast facial parts, such as eyes, nose and mouth, to be highlighted in a compact mode no matter the face is big or small. A neural-network based classifier is designed to decide a gradient energy pattern as face or non-face. In contrast to the traditional spatial-domain techniques, the proposed compressed domain technique eliminates the unnecessary decompression step and thus has lower computational complexity. Moreover, compared with the previous methods based on DCT compressed domain, the proposed multi-level gradient energy presentation removes the complex spatial scaling operation in compressed domain and overcomes block quantization problem. Based on test results of a variety of pictures, the presented algorithm was found to be more efficient and effective than the previous related methods.