Voice recognition is an important and active research area of the recent years. This research aims to build a system for voice recognition using dynamic time wrapping algorithm, by comparing the voice signal of the speaker with pre-stored voice signals in the database, and extracting the main features of the speaker voice signal using Mel-frequency cepstral coefficients, which is one of the most important factors in achieving high recognition accuracy. General TermsDynamic time wrapping "DTW" algorithm, Mel-frequency Cepstral Coefficients "MFCC" algorithm, vocal signal.

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Abstract-The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint The process of rrecognizing facial expression is a hard task due to the several circumstances such as facial occlusions, face shape, illumination, face colors, and etc. This paper present a PCA methodology to distinguish expressions of faces under different circumstances and identifying it. Relies on Eigen faces technique using standard Data base images. So as to overcome the problem of difficulty to computers to identify the features and expressions of persons.

Abstract:The process of handwriting recognition and text is an important field which have a large role in many applications, including the identification of manually written digits on checks and documents, also the recognition of the postal addresses using the technology of Optical Character Recognition (OCR), and etc. The aim of this paper is to using the linear correlation algorithms in two dimensions for the purpose of Arabic numerals (Indian) recognition (0-1-2-3-4-5-6-7-8-9). So as to overcome the problems of documents that stored in the form of image. And searching or editing it, In order to recognize the Arabic digits on them.

Abstract:The paper holding a presentation of a system, which is recognizing peoples through their iris print and that by using Linear Discriminant Analysis method. Which is characterized by the classification of a set of things in groups, these groups are observing a group the features that describe the thing, and is characterized by finding a relationship which give rise to differences in the dimensions of the iris image data from different varieties, and differences between the images in the same class and are less. This Prototype proves a high efficiency on the process of classifying the patterns, the algorithms was applied and tested on MMU database, and it gives good results with a ratio reaching up to 74%.