Detecting Speakers in Video Footage

Facial recognition is a powerful tool for identifying people visually. Yet, when the end goal is more specific than merely identifying the person in a picture problems can arise. Speaker identification is one such task which expects more predictive power out of a facial recognition system than can be provided on its own. Speaker identification is the task of identifying who is speaking in video not simply who is present in the video. This extra requirement introduces numerous false positives into the facial recognition system largely due to one main scenario. The person speaking is not on camera. This paper investigates a solution to this problem by incorporating information from a new system which indicates whether or not the person on camera is speaking. This information can then be combined with an existing facial recognition to boost its predictive capabilities in this instance. We propose a speaker detection system to visually detect when someone in a given video is speaking. The system relies strictly on visual information and is not reliant on audio information. By relying strictly on visual information to detect when someone is speaker the system can be synced with an existing facial recognition system and extend its predictive power. We use a two-stream convolutional neural network to accomplish the speaker detection. The neural network is trained and tested using data extracted from Digital Democracy’s large database of transcribed political hearings [4]. We show that the system is capable of accurately detecting when someone on camera is speaking with an accuracy of 87% on a dataset of legislators. Furthermore we demonstrate how this information can benefit a facial recognition system with the end goal of identifying the speaker. The system increased the precision of a existing facial recognition system by up to 5% at the cost of a large drop in recall

Spartan Face Mask Detection and Facial Recognition System

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices

Facial Recognition System Screening Evaluation Methodology For Complexion Biases

Konačna verzija diplomaskog rada. Cilj ovog diplomskog rada bila je sistematizacija pristupa razvoju višeplatformskih mobilnih igara uz pomoć Unity razvojnog okvira. Analizirane su aktivnosti članova tima unutar okvira odabranog pristupa razvoju softvera, utvrđene su one koje usporavaju proces razvoja te su predložena rješenja za te aktivnosti. U praktičnom dijelu rada napravljena je mobilna aplikacija u Unity alatu te je simuliran pristup razvoju mobilne igre u istom upotrebom metodike izabrane na temelju sistematizacije

Color-based facial recognition system

This paper develops an algorithm system to check whether the role of color can be an important attribute in facial recognition systems in two dimensions (2-D), with frontal orientation and small variations in the gestures of individuals. The first phase involves the detection and localization of the human face for which the learning algorithm uses a combination of AdaBoost and cascade classifiers to increase detection rates. In a second phase the eigenfaces approach is applied and a clasification system is implemented, to recognize and identify the subject of entry to a specific individual, using the Euclidean and Mahalanobis distance. We illustrate the results of the proposed system for both color images as gray, finding that the color information at the HSV plane can improve recognition rates when compared with the RGB plane.En este trabajo se desarrolla un sistema algorítmico con el fin de comprobar si el papel del color puede ser un atributo importante en los sistemas de reconocimiento facial en dos dimensiones (2-D), con orientación frontal y pequeñas variaciones en los gestos de los individuos. La primera fase consiste en la detección y localización del rostro humano para la cual se emplea el algoritmo de aprendizaje AdaBoost y una combinación de clasificadores en cascada, con el fin de aumentar las tasas de detección. En una segunda fase se aplica el enfoque de eigenfaces y se implementa un sistema clasificador para reconocer e identificar el sujeto de entrada a un individuo específico, utilizando la distancia euclidiana y de mahalanobis. Se ilustran los resultados obtenidos del sistema propuesto tanto para imágenes en color como en grises, contrastando que la información del color en el plano HSV puede mejorar las tasas de reconocimiento cuando se compara con el plano RGB

Designing facial recognition system with TinyML

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2022Technological advancement brought about improvements in the way IoT systems are implanted. The introduction of Tiny Machine Learning (TinyML) and edge computing has made it possible to process data at the edge and carry out machine learning processes at the edge without relying solely on cloud platforms for such purposes. Due to the advancement in technology, security can be improved and achieved at a cheaper cost. This project exploits TinyML technology to develop an access control system that uses facial recognition to control access to a room. The proposed system sends data that contains the name of the person who accessed the room and the time the room was accessed. This allows the user to monitor when and who accessed a room. The project focuses on the hardware aspect of the system. The system was able to grant access to authorized persons and can also be controlled from the web application.Ashesi Universit