Iris Recognition Using Scattering Transform and Textural Features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%

Face Detection and Recognition using Skin Segmentation and Elastic Bunch Graph Matching

Recently, face detection and recognition is attracting a lot of interest in areas such as network security, content indexing and retrieval, and video compression, because ‘people’ are the object of attention in a lot of video or images. To perform such real-time detection and recognition, novel algorithms are needed, which better current efficiencies and speeds. This project is aimed at developing an efficient algorithm for face detection and recognition. This project is divided into two parts, the detection of a face from a complex environment and the subsequent recognition by comparison. For the detection portion, we present an algorithm based on skin segmentation, morphological operators and template matching. The skin segmentation isolates the face-like regions in a complex image and the following operations of morphology and template matching help reject false matches and extract faces from regions containing multiple faces. For the recognition of the face, we have chosen to use the ‘EGBM’ (Elastic Bunch Graph Matching) algorithm. For identifying faces, this system uses single images out of a database having one image per person. The task is complex because of variation in terms of position, size, expression, and pose. The system decreases this variance by extracting face descriptions in the form of image graphs. In this, the node points (chosen as eyes, nose, lips and chin) are described by sets of wavelet components (called ‘jets’). Image graph extraction is based on an approach called the ‘bunch graph’, which is constructed from a set of sample image graphs. Recognition is based on a directly comparing these graphs. The advantage of this method is in its tolerance to lighting conditions and requirement of less number of images per person in the database for comparison

Extracción de puntos característicos del rostro para medidas antropométricas

En este documento se propone una técnica para la extracción de 22 puntos característicos del rostro, orientada a aplicaciones de antropometría. La técnica se fundamenta en la transformada wavelets-Gabor y el uso del algoritmo EBGM (del término en inglés: Elastic Bunch Graph Matching). Este último algoritmo fue modificado para que los puntos extraídos correspondan a puntos característicos del rostro, los cuales se utilizan comúnmente en medidas antropométricas faciales. Las modificaciones consisten en un conjunto de restricciones geométricas para ajustar inicialmente la ubicación de los centros de búsqueda, y posteriormente para la definición de la región de esta búsqueda. Los resultados mostraron que los puntos centrales del rostro presentan errores de ubicación inferiores al milímetro, lo cual es consistente con las medidas en antropometría facial directa

Skin Segmentation based Elastic Bunch Graph Matching for efficient multiple Face Recognition

This paper is aimed at developing and combining different algorithms for face detection and face recognition to generate an efficient mechanism that can detect and recognize the facial regions of input image. For the detection of face from complex region, skin segmentation isolates the face-like regions in a complex image and following operations of morphology and template matching rejects false matches to extract facial region. For the recognition of the face, the image database is now converted into a database of facial segments. Hence, implementing the technique of Elastic Bunch Graph matching (EBGM) after skin segmentation generates Face Bunch Graphs that acutely represents the features of an individual face enhances the quality of the training set. This increases the matching probability significantly.Comment: 10 Pages Advances in Computer Science, Engineering Applications, May, 201

Performances of proposed normalization algorithm for iris recognition

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications

Face recognition with variation in pose angle using face graphs

Automatic recognition of human faces is an important and growing field. Several real-world applications have started to rely on the accuracy of computer-based face recognition systems for their own performance in terms of efficiency, safety and reliability. Many algorithms have already been established in terms of frontal face recognition, where the person to be recognized is looking directly at the camera. More recently, methods for non-frontal face recognition have been proposed. These include work related to 3D rigid face models, component-based 3D morphable models, eigenfaces and elastic bunched graph matching (EBGM). This thesis extends recognition algorithm based on EBGM to establish better face recognition across pose variation. Facial features are localized using active shape models and face recognition is based on elastic bunch graph matching. Recognition is performed by comparing feature descriptors based on Gabor wavelets for various orientations and scales, called jets. Two novel recognition schemes, feature weighting and jet-mapping, are proposed for improved performance of the base scheme, and a combination of the two schemes is considered as a further enhancement. The improvements in performance have been evaluated by studying recognition rates on an existing database and comparing the results with the base recognition scheme over which the schemes have been developed. Improvement of up to 20% has been observed for face pose variation as large as 45°

Pattern recognition to detect fetal alchohol syndrome using stereo facial images

Fetal alcohol syndrome (FAS) is a condition which is caused by excessive consumption of alcohol by the mother during pregnancy. A FAS diagnosis depends on the presence of growth retardation, central nervous system and neurodevelopment abnormalities together with facial malformations. The main facial features which best distinguish children with and without FAS are smooth philtrum, thin upper lip and short palpebral fissures. Diagnosis of the facial phenotype associated with FAS can be done using methods such as direct facial anthropometry and photogrammetry. The project described here used information obtained from stereo facial images and applied facial shape analysis and pattern recognition to distinguish between children with FAS and control children. Other researches have reported on identifying FAS through the classification of 2D landmark coordinates and 3D landmark information in the form of Procrustes residuals. This project built on this previous work with the use of 3D information combined with texture as features for facial classification. Stereo facial images of children were used to obtain the 3D coordinates of those facial landmarks which play a role in defining the FAS facial phenotype. Two datasets were used: the first consisted of facial images of 34 children whose facial shapes had previously been analysed with respect to FAS. The second dataset consisted of a new set of images from 40 subjects. Elastic bunch graph matching was used on the frontal facial images of the study populaiii tion to obtain texture information, in the form of jets, around selected landmarks. Their 2D coordinates were also extracted during the process. Faces were classified using knearest neighbor (kNN), linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Principal component analysis was used for dimensionality reduction while classification accuracy was assessed using leave-one-out cross-validation. For dataset 1, using 2D coordinates together with texture information as features during classification produced a best classification accuracy of 72.7% with kNN, 75.8% with LDA and 78.8% with SVM. When the 2D coordinates were replaced by Procrustes residuals (which encode 3D facial shape information), the best classification accuracies were 69.7% with kNN, 81.8% with LDA and 78.6% with SVM. LDA produced the most consistent classification results. The classification accuracies for dataset 2 were lower than for dataset 1. The different conditions during data collection and the possible differences in the ethnic composition of the datasets were identified as likely causes for this decrease in classification accuracy