Face Recognition System Using Codebook Space Information Processing

采用矢量量化码本-空间信息处理算法，发展了一种新的人脸识别技术。“详细的处理步骤如下。首先将人脸图像划分为小尺寸块（4 × 4），通过减去块内的最小强度来提取差分强度信息。然后利用理论合成的码书进行矢量量化，通过统计分析各码书矢量的参考频率提取个人特征信息。最后，进行特征信息与数据库之间的匹配以识别人。我们已经开发出有效的过滤程序，以消除噪音和不需要的信号成分和直方图标准化程序来校准的脸的大小。另外，为了在识别率较高的区域改进识别算法，我们引入了“有效分辨距离”作为识别度量。最后，我们有r ...更多信息 对44个人的220幅人脸图像实现了100%的识别成功率，并将该识别算法应用于人脸表情识别中。在识别同一个人的3种面部表情（愤怒，快乐和正常）时，成功率达到100%。通过对合适的滤波器尺寸和图像分辨率的评估，发现在真实的空间中具有13 mm ~ 14 mm或更长周期的信号分量对于面部表情识别是非常重要的。我们已经实现了高性能的说话人识别算法，它利用倒谱分析的特征提取和分类的矢量量化。通过新开发的分层匹配方法和预学习过程，提高了识别速度和成功率。最后，我们实现了97%的识别成功率最高为58人的290文本无关的讲话。少

Novel faco rccognition technology has been developed using "Vector-Quatization Codebook-Space Information Processing Algorithm." Detailed processing steps are as follows. Facial image is first divided into small size blocks (4X4) and differential intensity information is extracted by subtracting minimum intensity within the blocks. Then vector quantization is carried out using theoretically synthesized codebook and personal feature information is extracted by statistically analyzing referred frequenucies of each codebook vector. Finally, matching between the feature information and the database is carried out to identify the person. We have developed effective filtering procedure to eliminate noise and unwanted signal component and histogram standardization procedure to calibrate the size of faces. In addition, we have introduced "effective discrimination distanace" as a recognition measture in order to improve the recognition algorithm at higher success rate region. Finally, we have r … More ealized 100% recognition success rate for 44 person's 220 facial images.We have applied the recognition algorithm into the facial expression recognition. 100% success rate has been realized in recognizing 3 facial expressions (anger, happiness, and normal) of the identical person. It is revealed by evaluating suitable filter size and image resolution that the signal components having a period of 13mm to 14 mm or longer at real space are very important for facial expression recognition.We have also studied a speaker recognition technology for realizing highly accurate human recognition and identification. We have realized high performance speaker recognition algorithm, which utilizes feature extraction by cepstrum analysis and classification by vector quantization. Improvements on recognition speed and success rate were achieved by newly developed hierarchical matching method and pre-learning procedure. Finally, we have realized 97% recognition success rate at maximum for 58 person's 290 text-independent speeches. Less