Abstract: User recognition is examined using neural and conventional techniques for processing speech and face images. This article for the first time attempts to overcome this significant problem of distortions inherently captured over multiple sessions (days). Speaker recognition uses both Linear Predictive Coding (LPC) cepstral and auditory neural model representations with speaker dependent codebook designs. For facial imagery, recognition is developed on a neural network that consists of a single hidden layer multilayer perceptron backpropagation network using either the raw data as inputs or principal components of the raw data computed using the Karhunen-Loeve Transform as inputs. The data consists of 10 subjects; each subject recorded utterances and had images collected for 10 days. The utterances collected were 400 rich phonetic sentences (4 sec), 200 subject name recordings (3 sec), and 100 imposter name recordings (3 sec). Face data consists of over 2000, 32 $MUL 32 pixel, 8 bit gray scale images of the 10 subjects. Each subsystem attains over 90% verification accuracy individually using test data gathered on days following the training data.!32
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机译：摘要：使用神经和常规技术处理语音和面部图像来检查用户识别。本文首次尝试克服在多个会话（工作日）中固有捕获的严重失真问题。说话人识别同时使用线性预测编码（LPC）倒谱和听觉神经模型表示，以及说话人相关的码本设计。对于面部图像，在神经网络上开发识别，该神经网络由单个隐藏层多层感知器反向传播网络组成，使用原始数据作为输入或使用Karhunen-Loeve变换计算的原始数据的主要成分作为输入。数据包括10个主题；每个受试者记录说话并收集了10天的图像。收集的话语包括400个丰富的语音句子（4秒），200个主题名称录音（3秒）和100个冒名顶替者录音（3秒）。人脸数据包含10个对象的2000张以上，32个$ MUL 32像素，8位灰度图像。每个子系统使用训练数据后几天收集的测试数据单独达到90％以上的验证准确性！32
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