Development of Deep Learning Algorithms for Improved Facial Recognition in Security Applications

This research aims to develop artificial intelligence (AI) algorithms in the context of facial recognition with a focus on increasing accuracy in difficult environmental conditions. Although facial recognition technology has made great progress, challenges such as poor lighting, variations in facial expressions, and head rotation are still problems that must be overcome. The research methodology involved collecting a wide dataset covering a wide variety of faces under various environmental conditions. This data is then processed and its features are extracted using computer image processing techniques. Furthermore, several deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were developed, trained, and evaluated for face recognition tasks. The expected result is the development of an AI algorithm that is able to overcome challenges in facial recognition with higher accuracy than existing methods. In particular, significant improvements in facial recognition accuracy are expected especially under low lighting conditions and variations in facial expressions. This research has a major impact in a variety of security applications, such as border surveillance, building access control, and corporate security. With higher facial recognition accuracy, security risks can be significantly reduced, resulting in safer and more efficient security solutions. In conclusion, this research aims to bring innovation in facial recognition technology through advanced AI approaches, with the potential to improve security in various contexts

IDENTIFIKASI WAJAH PADA SISTEM KEAMANAN BRANKAS MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS

Security becomes an inevitable requirement for users that require privacy. Is a distinguishing feature of biometric identity personally owned and have unique or special characteristics. Biometric characteristic can be used as a pointer of one's identity, especially the face. Facial recognition algorithms for computer-vision has been developed and widely applied. Principles component analysis has been shown to efficiently represent the state of human faces. In this final PCA method was applied as a method of face recognition to verify the user's control in a safe. From the activities carried out showed that the PCA as a method of face recognition for access control safes, properly used in environments with relatively fixed lighting conditions or no change with accuracy up to 79%. This method is still vulnerable to changes in lighting conditions so that the lighting environment in which the system operates need to be kept constant. Pose changes did not significantly affect the accuracy of face recognition with PCA method. As an access control method of PCA should be supported by setting the room lighting was kept constant. Keyword: PCA, Face Recognition

IoT-Based Access Management Supported by AI and Blockchains

Internet-of-Things (IoT), Artificial Intelligence (AI), and Blockchains (BCs) are essential techniques that are heavily researched and investigated today. This work here specifies, implements, and evaluates an IoT architecture with integrated BC and AI functionality to manage access control based on facial detection and recognition by incorporating the most recent state-of-the-art techniques. The system developed uses IoT devices for video surveillance, AI for face recognition, and BCs for immutable permanent storage to provide excellent properties in terms of image quality, end-to-end delay, and energy efficiency

Study of Different Algorithms for Face Recognition

The importance of utilising biometrics to establish personal authenticity and to detect impostors is growing in the present scenario of global security concern. Development of a biometric system for personal identification, which fulfils the requirements for access control of secured areas and other applications like identity validation for social welfare, crime detection, ATM access, computer security, etc., is felt to be the need of the day [2]. Face recognition has been evolving as a convenient biometric mode for human authentication for more than last two decades. It plays an important role in applications such as video surveillance, human computer interface, and face image database management [1]. A lot of techniques have been applied for different applications. Robustness and reliability becomes more and more important for these applications especially in security systems. Basically Face Recognition is the process through which a person is identified by his facial image. With the help of this technique it is possible to use the facial image of a person to authenticate him into any secure system. Face recognition approaches for still images can be broadly categorized into holistic methods and feature based methods. Holistic methods use the entire raw face image as an input, whereas feature based methods extract local facial features and use their geometric and appearance properties. This work studies the different approaches for a Face Recognition System. The different approaches like PCA, DCT and different types of Wavelets have been studied with the help of Euclidean distance as a classifier and Neural Network as a classifier. The results have been compared for the two database, AMP which contains 975 images of 13 individuals (each person has 75 different images) under various facial expressions and lightning condition with each image being cropped and resized to 64×64 pixels for the simulation and ORL (Olivetti Research Lab) which contains 400 images (each with 112×92 pixels) corresponding to 40 persons in 10 poses each including both male and female. The ORL database image has been resized to 128×128 pixels

Learning Domain Invariant Information to Enhance Presentation Attack Detection in Visible Face Recognition Systems

Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multi-spectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack detection systems may be discriminative, the need for additional sensors and computational resources substantially increases complexity and costs. Instead, we propose a method that exploits information from infrared imagery during training to increase the discriminability of visible-based presentation attack detection systems. We introduce (1) a new cross-domain presentation attack detection framework that increases the separability of bonafide and presentation attacks using only visible spectrum imagery, (2) an inverse domain regularization technique for added training stability when optimizing our cross-domain presentation attack detection framework, and (3) a dense domain adaptation subnetwork to transform representations between visible and non-visible domains. Adviser: Benjamin Rigga

A Review on Face Anti-Spoofing

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types

Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain

Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the current high accuracy is guaranteed by very deep network structures, facial images often need to be transmitted to third-party servers with high computational power for inference. However, facial images visually reveal the user's identity information. In this process, both untrusted service providers and malicious users can significantly increase the risk of a personal privacy breach. Current privacy-preserving approaches to face recognition are often accompanied by many side effects, such as a significant increase in inference time or a noticeable decrease in recognition accuracy. This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Due to the utilization of differential privacy, it offers a guarantee of privacy in theory. Meanwhile, the loss of accuracy is very slight. This method first converts the original image to the frequency domain and removes the direct component termed DC. Then a privacy budget allocation method can be learned based on the loss of the back-end face recognition network within the differential privacy framework. Finally, it adds the corresponding noise to the frequency domain features. Our method performs very well with several classical face recognition test sets according to the extensive experiments.Comment: ECCV 2022; Code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/dctd

Modeling and implementation of an automatic Access control system for secure permises using facial recognition

Security is a major concern within companies to prevent access to information by unauthorized persons.  In this work, we are interested in access control through facial recognition. To realize this access control system based on facial recognition, we used an embedded system under Arduino which gives us the possibility to assemble the performances of programming and electronics, more precisely, we programmed electronic systems for the automatic opening of doors without the action of a human being. From a sample of 100 individuals composed of 40 women and 60 men, 75 of whom were registered and 25 non-registered, our access control system obtained the results of 70 true positives, 5 false negatives, 8 false positives and 17 true negatives that constitute our confusion matrix. However, from the set of tests performed we can conclude that multi-modality fusion can be leveraged to increase the performance of the verification system as the verification performance of multimodal systems (feature fusion or score fusion) can be applied to give even better results

Sistema de reconocimiento facial para el control de acceso en la I.E 81585 Sagrado Corazón de Jesús de Cartavio - Ascope - La Libertad en el segundo y tercer bimestre del año lectivo 2022

La presente tesis titulada Sistema de Reconocimiento Facial para el control de acceso en la I. E. 81585 Sagrado Corazón de Jesús de Cartavio – Ascope – La Libertad en el segundo y tercer bimestre del año lectivo 2022 de autoría del bachiller Fernando Rafael Casana Raymundo, cuyo problema ha sido la falta de un control de acceso efectivo y eficiente. Actualmente el control de acceso de personal y alumnado de la I. E. Sagrado Corazón de Jesus no ofrece la confiabilidad necesaria lo cual implica que exista la posibilidad de la suplantación de identidad en el momento de ingresar y salir por ello, en esta investigación se tiene como objetivo el desarrollo de un sistema de reconocimiento facial que permita un control de acceso superior al que tiene la I. E. Un sistema de reconocimiento facial consta de tres fases básicas: adquisición de imágenes, procesamiento de imágenes, extracción de características e identificación de personas, ya que al tener identificada a la persona se posee menos riesgo de que ingresen personas desconocidas. Para solucionar este problema se seleccionó el mejor método de acuerdo a los requerimientos de la I. E., se utilizó OpenCV que es una librería de visión artificial, que nos permite procesar imágenes, así obteniendo las características esenciales de los rostros para así poder identificarlas haciendo uso del modelo Eingenfaces, el cual será entrenado con las imágenes del personal y alumnado de la I. E. Finalmente, las personas identificadas tuvieron un rectángulo verde encerrando su rostro y su nombre en la parte superior del rectángulo con su valor de confianza. El desarrollo de este sistema utilizando PCA, Eingenfaces y las librerías de Open CV permitió elaborar un sistema con reconocimiento facial para el control de acceso, el cual permitió mejorar las entradas y salidas de las personas de la I. E. en la cual se puede visualizar las imágenes de prueba así mismo se pudo ver que el software de reconocimiento facial reconoce a las personas con seguridadThis thesis entitled Facial Recognition System for access control in the I. E. 81585 Sagrado Corazón de Jesús de Cartavio - Ascope - La Libertad in the second and third bimester of the 2022 school year by the bachelor Fernando Rafael Casana Raymundo, whose problem has been the lack of effective and efficient access control. Currently the access control of staff and students of the I. E. Sagrado Corazón de Jesus does not offer the necessary reliability which implies that there is the possibility of identity theft at the time of entering and exiting for it, in this investigation it has as objective the development of a facial recognition system that allows access control superior to that of the I.E. A facial recognition system consists of three basic phases: image acquisition, image processing, feature extraction and person identification, since by having the person identified there is less risk of unknown persons entering. To solve this problem, the best method was selected according to the requirements of the I.E., OpenCV was obtained, which is an artificial vision library, which allows us to process images, thus obtaining the essential characteristics of the faces in order to be able to identify them using the Eingenfaces model, which will be disturbed with the images of the staff and students of the I. E. Finally, the identified people had a green rectangle enclosing their face and their name in the upper part of the rectangle with their trust value. The development of this system using PCA, Eingenfaces and the Open CV libraries was able to develop a system with facial recognition for access control, which was able to improve the entrances and exits of people from the I. E. in which the test images can be viewed, as well as it was possible to see that the software of facial recognition recognizes people safelyTesi