Asaad Noori Hashim Al-Shareefi

—Object detection and recognition is one of the important techniques in computer vision for searching and scanning and identifying an object in images or videos. Object detection and recognition enters into many important fields where one of the uses of object detection and recognition is to detect region of injury and determine the type of injury. This paper suggested a new effective method called Local Quadrant Pattern (LQP). The proposed method uses a window and passes it on all pixels of the image and uses the pixel direction to arrange the adjacent pixels. It also uses four code values to encode and then produce a texture feature matrix which is used to detect objects as well as extract features based on magnitude of pixels for image classification. The experiments were conducted on the infected regions in the skin and the results showed the ability of the method to detect regions of infection as well as the high accuracy in the classification of those regions.

Abstract: As one of the more effective applications that used in image understanding and analysis, face recognition has given significant attention in last years. There are several methods used for face recognition such as PCA, LDA, Zernike and each method has limitations and strength points. This study offers a statistical estimate of the execution to recognized the human faces in digital images by using a new feature extraction method which based on hybrid system for face recognition that contains: Gabor filters and singular value decomposition. By using the Gabor filters 40 sub-images were obtained from the original images in 5 scales and 8 orientations and by SVD using one matrix U from 3 matrices [USV] that have singular value which represent feature extracted from an image and using hybrid technique normalize features vectors by Z-score to get optimal values then fusion two features vectors 2D Gabor filter and SVD to optimize the recognition rate. Finally, classification step is done by (Euclidean distance) to take the decision about matching. The outcomes experimental showed that the suggest system is effective, it has been tested using ORL face images databases with 10 cases and achieved recognition rate from 77.7-100%, also, applied on FEI Brazil face database with 5 cases and achieved recognition rate from to 84-100%.

Iris recognition is a biometric technique that uses iris pattern information to detect person identification. Initially, the system find out the boundary of the pupil and iris. Then, Circular Hough transform used to find out the center of both pupil and iris in order to crop iris part from the eye image. After that, Daugman’s Rubber Sheet model utilized for performing the normalizing step. Then, features extracted based on Legendre moment and Local Quantized. Several orders value with many region of iris have been used to get best value, which satisfied the highest recognition rate.Matching was performed by City Block Distance. The sim

Face Recognition Building new system for (face recognition problem).A system based on the integration of the following methods: _ SVD, which is used to extract three matrices, one of these matrices depends on the rows and the other on the columns and the last on the rows and columns together. The three previous matrices are considered to derive image properties. The second way is to convert the image to a single matrix. It depends on the angle. Which makes it convert the database into base properties (the person’s term varies and varies with the other)? And then use a new equation for the classification.

Face Recognition according to (NLS), result from two previous system put them to gather to become (NLS), this system is very useful and depends on (Essam.H system and Novel System), (NLS) applied on two types of databases (ORL) and (FEI) and its give a good result for Face Recognition ratio, when applied on ORL its give 100%, (FEI) consist of 2800 images, put each 700 images to become sub_database_FEI, first_sub_database will give 98%, second_sub_database will give 100%, and finally (third_sub_database+ 14 poses for me) will give 98.039216%.

Abstract: The aim of this study is proposed a new IRS using hybrid methods. These methods used to extract features of tested eye images. Gabor wavelet and Zernike moment used to extract features of iris. Canny edge detection and Hough transform used to determine the iris. The proposed system tested on CASIA!v4.0 interval database. The results show that the proposed method having good accuracy about 97%. PSNR applied on the training and testing iris image to measure the simmilarity between them. PSNR is support the proposed system where, highest value of PSNR for the tesed image dells with the image is belong to the same person in training database.

New paper has been bublished about iris recognition