Essam.H system for Face Recognition

A face recognition system is one of the biometric information processes, its applicability is easier and working range is larger than others, i.e.; fingerprint, iris scanning, signature, etc. A face recognition system is designed, implemented and tested at Atılım University, Mechatronics Engineering Department. The system uses a combination of techniques in two topics; face detection and recognition. The face detection is performed on live acquired images without any application field in mind. Processes utilized in the system are white balance correction, skin like region segmentation, facial feature extraction and face image extraction on a face candidate. Then a face classification method that uses FeedForward Neural Network is integrated in the system. The system is tested with a database generated in the laboratory with 26 people. The tested system has acceptable performance to recognize faces within intended limits. System is also capable of detecting and recognizing multiple faces in live acquired images.

Face recognition plays vital role for authenticating system. Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition has attracted much attention due to its potential value in security and law enforcement applications and its theoretical challenges. Different methods are used for feature extraction and classification. Kernel fisher analysis is used for feature extraction. The performance analysis for Euclidean, support vector machine is evaluated. The whole process is done using MATLAB software. A set of 10 person real time images is taken for our work. The classifier recognizes the similar posture as an output.

Darcan, E & Aydogan, H., Facial Recognition Technology. Harvey, K. (Ed.). (2014). Encyclopedia of Social Media and Politics (Vol. 1). SAGE Publications.

Face recognition systems are still an effective law-enforcement tool, despite their flaws in accuracy of identification. It is still a very new technology that is being developed, and researchers are constantly trying to optimize its efficiency in accurately identifying people. In the future, face recognition systems may be perfected and become more functional. They may help find missing persons, track down fugitives, and secure national borders. Face recognition systems could provide a more convenient and safe world in the future. Hakan Aydogan Emirhan Darcan Rutgers, The State University of New Jersey

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euroasiapub.org

As in the environment importance of security is increasing. So for organization identification and authentication is also important. The face recognition methods have become a key technology in various areas: access control in buildings; access control for computer in general or for ATMs in particular day-to-day affairs like withdrawing money from bank account or dealing with the post office; or in the prominent field of criminal investigation. Such requirement for reliable personal identification in computerized access control has resulted in an increased in biometrics and in the main field the face recognition. Face recognition is a biometric method of identifying a person based on a photograph of his face. The quality of the computer recognition system is dependent on the quality of the image and mathematical algorithms used to convert a picture into numbers. Important factors for the image quality are light, background, and position of the head. There are different kinds of algorithms for the face recognition. The different algorithms for the face recognition differ in their behaviours, but the analysis follows the same steps. The first step is image acquisition; once the image is captured, a head is identified. In some cases, before the feature extraction, it might be necessary to normalize the image, and then begins feature extraction using one of the algorithms. For any given computer vision problem, there are numerous algorithms designed to solve it. The design of each algorithm is based on a set of decisions and assumptions. These algorithms are able to recognise the face from small and large database. These algorithms have different features and are 2-D or 3-D based. These algorithms are divided in different categories based on the approach used for the face recognition. Also different types of databases are available for face recognition like FERET, Asian and Korean etc.

—Face recognition is a very active domain in computer vision and in Biometrics. It is a biometric modality that has attracted huge interest in the automatic processing of digital images and videos in many applications, including biometric identification, video-surveillance, human-computer interaction and multimedia data management. Face recognition usually involves three key processes in its treatment: face detection, feature extraction and classification. In this article, we focus on the study and synthesis of the classification methods most widely used in face recognition, namely: metric distances, neural networks and Supports Vectors Machines (SVM).

Advances in intelligent systems and computing, 2019

In this paper, we have proposed an efficient and computationally inexpensive approach toward two mainstreams of image recognition, i.e, face recognition and person identification. Our proposed model is invariant to pose, expression, scale,

A wide variety of systems require reliable person recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user and no one else access the rendered services. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. Face can be used as Biometrics for person verification. Face is a complex multidimensional structure and needs a good computing techniques for recognition. We treats face recognition as a two-dimensional recognition problem. A well-known technique of Principal Component Analysis (PCA) is used for face recognition. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by Eigen face which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, lips. The system performs by projecting pre extracted face image onto a set of face space that represent significant variations among known face images. The variable reducing theory of PCA accounts for the smaller face space than the training set of face. A Multire solution features based pattern recognition system used for face recognition based on the combination of Radon and wavelet transforms. As the Radon transform is in-variant to rotation and a Wavelet Transform provides the multiple resolution. This technique is robust for face recognition. The technique computes Radon projections in different orientations and captures the directional features of face images. Further, the wavelet transform applied on Radon space provides multire solution features of the facial images. Being the line integral, Radon transform improves the low-frequency components that are useful in face recognition.