Finding faces for Gender classification using BPNN AND PCA based recognition

Face Classification system is a computer program which will classify a face into different categories as per certain criterias. We will be training our systems to classify the images into two septum. The first would classify the images based on human or non--human characteristics. The second category of classification would be classifying the human faces as male or female face. We will implement this using Back Propagation Neural Network algorithm. This pre-classification will help in reducing the total time required to recognize an image and hence increasing the overall speed. Face Recognition system is a computer applicaton that identifies a face of a person in a digital image by comparing the face in the image with the facial database of some trained images. This system can recognize images of a person with emotions and expressions different with those in the facial database. We will implement this using a mathematical tool called Principal Component Analysis and Mahalanobis Distance Algorithm. It can be used for security purposes at restricted places by granting access to only authorised persons. It can also be used for criminal identification

IMPLEMENTASI SISTEM PENGENALAN WAJAH UNTUK KEAMANAN AKSES BERBASIS UBUNTU MENGGUNAKAN PYTHON

Security is one of the most important needs for human beings in both the building and the house. For the development of security technology used face recognition. Face recognition is a system that identifies facial features that are capable of detecting familiar faces and unknown faces. In this research is implemented with computer vision where the computer can see and understand so that it is information from an image or video. This computer can also mimic the ability of human intelligence. To classify a face object, OpenCv uses the Haar Cascade classifier and uses Python programming language. Application used face Recognition program is PyCharm Comunity 2018 version 3 with Linux operating system Ubuntu 18.04.2 LTS version. The results showed that the accuracy of face reconition depends on the analysis of OpenCv and the classification of Cascade for computer vision process

Embedded Face Detection and Facial Expression Recognition

Face Detection has been applied in many fields such as surveillance, human machine interaction, entertainment and health care. Two main reasons for extensive attention on this typical research domain are: 1) a strong need for the face recognition system is obvious due to the widespread use of security, 2) face recognition is more user friendly and faster since it almost requests the users to do nothing. The system is based on ARM Cortex-A8 development board, including transplantation of Linux operating system, the development of drivers, detecting face by using face class Haar feature and Viola-Jones algorithm. In the paper, the face Detection system uses the AdaBoost algorithm to detect human face from the frame captured by the camera. The paper introduces the pros and cons between several popular images processing algorithm. Facial expression recognition system involves face detection and emotion feature interpretation, which consists of offline training and online test part. Active shape model (ASM) for facial feature node detection, optical flow for face tracking, support vector machine (SVM) for classification is applied in this research

Real time face matching with multiple cameras using principal component analysis

Face recognition is a rapidly advancing research topic due to the large number of applications that can benefit from it. Face recognition consists of determining whether a known face is present in an image and is typically composed of four distinct steps. These steps are face detection, face alignment, feature extraction, and face classification [1]. The leading application for face recognition is video surveillance. The majority of current research in face recognition has focused on determining if a face is present in an image, and if so, which subject in a known database is the closest match. This Thesis deals with face matching, which is a subset of face recognition, focusing on face identification, yet it is an area where little research has been done. The objective of face matching is to determine, in real-time, the degree of confidence to which a live subject matches a facial image. Applications for face matching include video surveillance, determination of identification credentials, computer-human interfaces, and communications security. The method proposed here employs principal component analysis [16] to create a method of face matching which is both computationally efficient and accurate. This method is integrated into a real time system that is based upon a two camera setup. It is able to scan the room, detect faces, and zoom in for a high quality capture of the facial features. The image capture is used in a face matching process to determine if the person found is the desired target. The performance of the system is analyzed based upon the matching accuracy for 10 unique subjects

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.publishedVersio

Pose Invariant Face Recognition and Tracking for Human Identification

Real-time tracking and recognition of people in complex environments has been a widely researched area in computer vision as it has a huge potential in efficient security automation and surveillance. We propose a real time system for detection and recognition of individuals in a scene by detecting, recognizing and tracking faces. The system integrates the multi-view face detection algorithm, the multi-pose face recognition algorithm and the extended multi-pose Kalman face tracker. The multi-view face detection algorithm contains the frontal face and profile face detectors which extract the Haar-like features and detect faces at any pose by a cascade of boosted classifiers. The pose of the face is inherently determined from the face detection algorithm and is used in the multi-pose face recognition module where depending on the pose, the detected face is compared with a particular set of trained faces having the same pose range. The pose range of the trained faces is divided into bins onto which the faces are sorted and each bin is trained separately to have its own Eigenspace. The human faces are recognized by projecting them onto a suitable Eigenspace corresponding to the determined pose using Weighted Modular Principal Component Analysis (WMPCA) technique and then, are tracked using the proposed multiple face tracker. This tracker is implemented by extracting suitable face features which are represented by a variant of WMPCA and then tracking these features across the scene using the Kalman filter. This low-level system is created using the same face database of twenty unrelated people trained using WMPCA and classification is performed using a feature correlation metric. This system has the advantage of recognizing and tracking an individual in a cluttered environment with varying pose variations.https://ecommons.udayton.edu/stander_posters/1240/thumbnail.jp

Fast, collaborative acquisition of multi-view face images using a camera network and its impact on real-time human identification

Biometric systems have been typically designed to operate under controlled environments based on previously acquired photographs and videos. But recent terror attacks, security threats and intrusion attempts have necessitated a transition to modern biometric systems that can identify humans in real-time under unconstrained environments. Distributed camera networks are appropriate for unconstrained scenarios because they can provide multiple views of a scene, thus offering tolerance against variable pose of a human subject and possible occlusions. In dynamic environments, the face images are continually arriving at the base station with different quality, pose and resolution. Designing a fusion strategy poses significant challenges. Such a scenario demands that only the relevant information is processed and the verdict (match / no match) regarding a particular subject is quickly (yet accurately) released so that more number of subjects in the scene can be evaluated.;To address these, we designed a wireless data acquisition system that is capable of acquiring multi-view faces accurately and at a rapid rate. The idea of epipolar geometry is exploited to get high multi-view face detection rates. Face images are labeled to their corresponding poses and are transmitted to the base station. To evaluate the impact of face images acquired using our real-time face image acquisition system on the overall recognition accuracy, we interface it with a face matching subsystem and thus create a prototype real-time multi-view face recognition system. For front face matching, we use the commercial PittPatt software. For non-frontal matching, we use a Local binary Pattern based classifier. Matching scores obtained from both frontal and non-frontal face images are fused for final classification. Our results show significant improvement in recognition accuracy, especially when the front face images are of low resolution