Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model

يعتبر تمييز الوجه مجالًا نشطًا لعلوم التصوير. ومع التطورات الحديثة في تطوير رؤية الكمبيوتر ، يتم تطبيقه على نطاق واسع في مختلف المجالات ، وخاصة في فرض القانون والأمن. ان الوجه البشري مقياس حيوي يمكن استخدامه بفعالية في كل من تحديد الهوية والتحقق منها. حتى الآن ، وبغض النظر عن نموذج الوجه والمقاييس ذات الصلة المستخدمة ، فإن عيبه الرئيس هو أنه يتطلب صورة للوجه ، يتم إجراء المقارنة عليها. لذلك ، هناك حاجة دائمًا إلى أجهزة تلفزيون الدائرة المغلقة وقاعدة بيانات الوجه في نظام التشغيل. وللأسف خلال العقود القليلة الماضية ، شهدنا ظهور حرب غير متكافئة ، حيث يتم ارتكاب أعمال إرهابية في كثير من الأحيان في منطقة منعزلة بدون كاميرا مثبتة وربما بواسطة أشخاص لم يتم حفظ صورهم في أي قاعدة بيانات رسمية قبل الحدث. خلال التحقيقات اللاحقة ، كان على السلطات بالتالي الاعتماد على شهود مصابين بصدمات نفسية واحباط ، وهؤلاء تعتبر شهادتهم مشكوك فيها وغالبًا ما تكون مضللة بشأن ظهور المشتبه فيه. لمعالجة هذه المشكلة ، تقدم هذه الورقة تطبيقًا لنموذج المظهر الإحصائي للوجه الإنساني في المساعدة على تحديد هوية المشتبه به استنادًا إلى التذكر البصري للشاهد. تم تنفيذ نظام نموذج أولي عبر الإنترنت لإظهار وظائفه الأساسية. أشار كل من التقييمات المرئية والعددية الواردة هنا بشكل واضح إلى الفوائد المحتملة للنظام للغرض المقصود.Facial recognition has been an active field of imaging science. With the recent progresses in computer vision development, it is extensively applied in various areas, especially in law enforcement and security. Human face is a viable biometric that could be effectively used in both identification and verification. Thus far, regardless of a facial model and relevant metrics employed, its main shortcoming is that it requires a facial image, against which comparison is made. Therefore, closed circuit televisions and a facial database are always needed in an operational system. For the last few decades, unfortunately, we have experienced an emergence of asymmetric warfare, where acts of terrorism are often committed in secluded area with no camera installed and possibly by persons whose photos have never been kept in any official database prior to the event. During subsequent investigations, the authorities thus had to rely on traumatized and frustrated witnesses, whose testimonial accounts regarding suspect’s appearance are dubious and often misleading. To address this issue, this paper presents an application of a statistical appearance model of human face in assisting suspect identification based on witness’s visual recollection. An online prototype system was implemented to demonstrate its core functionalities. Both visual and numerical assessments reported herein evidentially indicated potential benefits of the system for the intended purpose

Robust single-sample face recognition by sparsity-driven sub-dictionary learning using deep features

Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative \u2113 0 -norm minimization algorithm called k-LIMAPS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8 7 8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations

Biometric fusion methods for adaptive face recognition in computer vision

PhD ThesisFace recognition is a biometric method that uses different techniques to identify the individuals based on the facial information received from digital image data. The system of face recognition is widely used for security purposes, which has challenging problems. The solutions to some of the most important challenges are proposed in this study. The aim of this thesis is to investigate face recognition across pose problem based on the image parameters of camera calibration. In this thesis, three novel methods have been derived to address the challenges of face recognition and offer solutions to infer the camera parameters from images using a geomtric approach based on perspective projection. The following techniques were used: camera calibration CMT and Face Quadtree Decomposition (FQD), in order to develop the face camera measurement technique (FCMT) for human facial recognition. Facial information from a feature extraction and identity-matching algorithm has been created. The success and efficacy of the proposed algorithm are analysed in terms of robustness to noise, the accuracy of distance measurement, and face recognition. To overcome the intrinsic and extrinsic parameters of camera calibration parameters, a novel technique has been developed based on perspective projection, which uses different geometrical shapes to calibrate the camera. The parameters used in novel measurement technique CMT that enables the system to infer the real distance for regular and irregular objects from the 2-D images. The proposed system of CMT feeds into FQD to measure the distance between the facial points. Quadtree decomposition enhances the representation of edges and other singularities along curves of the face, and thus improves directional features from face detection across face pose. The proposed FCMT system is the new combination of CMT and FQD to recognise the faces in the various pose. The theoretical foundation of the proposed solutions has been thoroughly developed and discussed in detail. The results show that the proposed algorithms outperform existing algorithms in face recognition, with a 2.5% improvement in main error recognition rate compared with recent studies