scholarly journals GMM vs SVM for Face Recognition and Face Verification

10.5772/22181 ◽  
2011 ◽  
Author(s):  
Jesus Olivares-Mercado ◽  
Gualberto Aguilar ◽  
Karina Toscano-Medina ◽  
Mariko Nakano ◽  
Hector Perez
Keyword(s):  
Face Recognition ◽  
Face Verification

scholarly journals Liveness Detection Based on Human eye Blinking for Photo Attacks

2019 ◽  
Vol 9 (1) ◽  
pp. 4074-4077
Keyword(s):  
Face Recognition ◽  
Aspect Ratio ◽  
Face Verification ◽  
Human Face ◽  
Eye Blink ◽  
Liveness Detection ◽  
Small Window ◽  
Time Period ◽  
Accuracy Result ◽  
Security Check

Auto face recognition mainly implemented to avoid the replication of identity to demonstrate through security check. This rage of face verification has brought intensive interest about facial biometric towards attacks of spoofing, in which a person’s mask or photo can be produced to be authorized. So, we propose a liveness detection based on eye blinking, where eyes are extracted from human face. The method of face recognition was applied by utilizing OpenCV classifier and dlib library, and a concept of edge detection and calculation of structure to extract the portion of the eye and to observe and make note of variation in the attributes of the eyes over a time period was employed. The landmarks are plotted accurately enough to derive the state of eye if it is closed or opened. A scalar quantity EAR (eye aspect ratio) is derived from landmark positions defined by the algorithm to identify a blink corresponding to every frame. The set of EAR values of successive frames are detected as a eye blink by a OpenCV classifier displayed on a small window when person is in front of camera. Finally, it gives the accuracy result whether it is human being or spoof attack.

scholarly journals Pairwise Ranking Distillation for Deep Face Recognition

2020 ◽  
pp. paper30-1-paper30-13
Author(s):  
Mikhail Nikitin ◽  
Vadim Konushin ◽  
Anton Konushin
Keyword(s):  
Face Recognition ◽  
Metric Learning ◽  
High Capacity ◽  
Recognition Task ◽  
Learning Task ◽  
Face Verification ◽  
Baseline Method ◽  
Task Knowledge ◽  
Model Compression ◽  
Knowledge Distillation

This work addresses the problem of knowledge distillation for deep face recognition task. Knowledge distillation technique is known to be an effective way of model compression, which implies transferring of the knowledge from high-capacity teacher to a lightweight student. The knowledge and the way how it is distilled can be defined in different ways depending on the problem where the technique is applied. Considering the fact that face recognition is a typical metric learning task, we propose to perform knowledge distillation on a score-level. Specifically, for any pair of matching scores computed by teacher, our method forces student to have the same order for the corresponding matching scores. We evaluate proposed pairwise ranking distillation (PWR) approach using several face recognition benchmarks for both face verification and face identification scenarios. Experimental results show that PWR not only can improve over the baseline method by a large margin, but also outperforms other score-level distillation approaches.

Hausdorff Distance for Face Recognition

2015 ◽  
pp. 39-46
Keyword(s):  
Face Recognition ◽  
Hausdorff Distance ◽  
Similarity Measures ◽  
Face Verification ◽  
3D Data ◽  
Modified Hausdorff Distance

When two sets are differently sized, the Hausdorff distance can be computed between them, even if the cardinality of one set is infinite. Different versions of this distance have been proposed and employed for face verification, among which the modified Hausdorff distance is the most famous. The important point to be noted is that, among the most commonly used similarity measures, the Hausdorff distance is the only one that has been widely applied to 3D data.

scholarly journals Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Holger Steiner ◽  
Sebastian Sporrer ◽  
Andreas Kolb ◽  
Norbert Jung
Keyword(s):  
Face Recognition ◽  
Imaging System ◽  
Short Wave ◽  
Classification Performance ◽  
High Spectral Resolution ◽  
Face Verification ◽  
Camera System ◽  
Spectral Signatures ◽  
Face Images ◽  
Small Band

Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published.

scholarly journals Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4575
Author(s):  
Fitri Arnia ◽  
Maulisa Oktiana ◽  
Khairun Saddami ◽  
Khairul Munadi ◽  
Roslidar Roslidar ◽  
...  
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Recognition Performance ◽  
Extraction Process ◽  
Face Verification ◽  
Surveillance Systems ◽  
Long Distance ◽  
Face Images ◽  
Homomorphic Filtering ◽  
Band Limited

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.

scholarly journals Secure Connected Transactions using Face Verification

2020 ◽  
Vol 9 (5) ◽  
pp. 165-167
Keyword(s):  
Face Recognition ◽  
Current System ◽  
Present Situation ◽  
Face Verification ◽  
Secret Key ◽  
Online Exchange ◽  
The Individual

In this day and age, cash can be required whenever or anyplace, for example, shopping, voyaging or wellbeing crises and so on. That additionally expands the danger of getting robed. Bank is a most secure spot to keep cash. In any case, consider the possibility that somebody will take your card and by one way or another he/she will know your secret key, it will give him/her full access to your cash. According to the present situation the online exchange is secure with one time secret word (OTP). In age of OTP there are numerous variables that can make OTP special each time it is produced. Right now execute client Identification utilizing Face Recognition to confirm the client. If there should be an occurrence of crisis circumstance the login should be possible utilizing OTP and furthermore the individual picture is caught and Mail to the Account Holder. Accordingly our framework is improve in Security contrasting with the current System.

scholarly journals Distillation of an End-to-End Oracle for Face Verification and Recognition Sensors

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1369
Author(s):  
Francesco Guzzi ◽  
Luca De Bortoli ◽  
Romina Soledad Molina ◽  
Stefano Marsi ◽  
Sergio Carrato ◽  
...  
Keyword(s):  
Face Recognition ◽  
Complex Model ◽  
Face Verification ◽  
Security Systems ◽  
Alignment Procedure ◽  
Open Set ◽  
Minimal Structure ◽  
Sensor Module ◽  
Network Methods ◽  
End To End

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.

scholarly journals xCos: An Explainable Cosine Metric for Face Verification Task

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-16
Author(s):  
Yu-Sheng Lin ◽  
Zhe-Yu Liu ◽  
Yu-An Chen ◽  
Yu-Siang Wang ◽  
Ya-Liang Chang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Recognition Task ◽  
High Accuracy ◽  
Face Verification ◽  
Verification Task ◽  
Deep Convolutional Neural Networks ◽  
Explainable Ai ◽  
The Face ◽  
Very High

We study the XAI (explainable AI) on the face recognition task, particularly the face verification. Face verification has become a crucial task in recent days and it has been deployed to plenty of applications, such as access control, surveillance, and automatic personal log-on for mobile devices. With the increasing amount of data, deep convolutional neural networks can achieve very high accuracy for the face verification task. Beyond exceptional performances, deep face verification models need more interpretability so that we can trust the results they generate. In this article, we propose a novel similarity metric, called explainable cosine ( xCos ), that comes with a learnable module that can be plugged into most of the verification models to provide meaningful explanations. With the help of xCos , we can see which parts of the two input faces are similar, where the model pays its attention to, and how the local similarities are weighted to form the output xCos score. We demonstrate the effectiveness of our proposed method on LFW and various competitive benchmarks, not only resulting in providing novel and desirable model interpretability for face verification but also ensuring the accuracy as plugging into existing face recognition models.

scholarly journals Face Frontalization

2021 ◽  
Vol 9 (VI) ◽  
pp. 5423-5433
Author(s):  
Cherukupally Sarika
Keyword(s):  
Face Recognition ◽  
Face Verification ◽  
Memory Space ◽  
Solitary Individual ◽  
Information Base ◽  
Proposed Model ◽  
The Face ◽  
Specific Individual

Face recognition is generally utilized in PC vision. The majority of the inserted and electronic gadgets are utilizing the face verification for security purposes. FR is utilized to distinguish an individual in a video or advanced picture. To actualize this we have to have a lot of pictures of a specific individual in information base with various face stances and appearances. For this cycle it expends huge memory space to store various pictures of a solitary individual. The info profile picture should coordinate with the picture present in the information base if not the face won't be perceived.Our proposed model will decrease the need of putting away different pictures of a solitary individual. In the event that the information picture is a non-frontal picture, at that point this model will change over that picture into frontal picture. Here info picture will go through a few picture handling procedures. Picture is analog in nature which speak to consistent territory if position and force esteems.

Face Deduplication in Video Surveillance

2017 ◽  
Vol 32 (03) ◽  
pp. 1856001 ◽  
Author(s):  
Qi Chen ◽  
Li Yang ◽  
Dongping Zhang ◽  
Ye Shen ◽  
Shuying Huang
Keyword(s):  
Face Recognition ◽  
Video Surveillance ◽  
Surveillance System ◽  
Quality Evaluation ◽  
Face Image ◽  
Face Verification ◽  
Identification Methods ◽  
Verification Method ◽  
Security Industry ◽  
Face Images

The video surveillance system based on face analysis has played an increasingly important role in the security industry. Compared with identification methods of other physical characteristics, face verification method is easy to be accepted by people. In the video surveillance scene, it is common to capture multiple faces belonging to a same person. We cannot get a good result of face recognition if we use all the images without considering image quality. In order to solve this problem, we propose a face deduplication system which is combined with face detection and face quality evaluation to obtain the highest quality face image of a person. The experimental results in this paper also show that our method can effectively detect the faces and select the high-quality face images, so as to improve the accuracy of face recognition.

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