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Detecting faces in the wavelet compressed domain

机译:小波压缩域中的人脸检测

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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.
机译:提出了一种可以在小波压缩域直接实现人脸检测的新技术。该算法以JPEG2000图片的熵解码和逆量化的小波变换系数为输入,并输出检测到的脸部位置。这项工作的主要贡献在于基于小波压缩数据提出了一种多层次梯度能量表示的人脸模式,无论是紧凑模式下,都可以突出显示相关的高对比度脸部,例如眼睛,鼻子和嘴巴脸大或小。基于神经网络的分类器被设计为将梯度能量模式确定为人脸或非人脸。与传统的空间域技术相比,所提出的压缩域技术消除了不必要的解压缩步骤,因此具有较低的计算复杂度。此外,与以前的基于DCT压缩域的方法相比,本文提出的多级梯度能量表示方法消除了压缩域中复杂的空间缩放操作,并克服了块量化问题。根据各种图片的测试结果,发现该算法比以前的相关方法更加有效。

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