Techniques and Challenges of Face Recognition: A Critical Review

Singh, Shilpi; Prasad, Srinivas

doi:10.1016/j.procs.2018.10.427

Cited by 93 publications

(42 citation statements)

References 35 publications

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“…The dual authentication framework strengthens the biometric face authentication due to different limitations in face recognition such as spoofing attacks [51]. The efforts for solving spoofing attacks especially on fingerprint and face recognition are still limited [12], and [30] due to the availability of user's image on public websites.…”

Section: Dual Authentication Frameworkmentioning

confidence: 99%

A Silent Password Recognition Framework Based on Lip Analysis

2020

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Securing passwords either written or spoken is considered one of the challenging authentication issues faced by individuals and organizations. Written passwords could be easily stolen by look over, or manbehind, while spoken passwords could be recorded and replayed by attackers. A proposed silent password which is based on a dual security model for lip movement analysis will be a promoting solution to these attacks. The goal of the current research is to propose a hybrid voting framework for silent passwords recognition using lip movement analysis. The proposed framework is built for Arabic language by extracting figures from predefined Arabic lexicon. The predefined lexicon mainly contains the Arabic figures from zero to nine with different shapes. The framework takes a video or sequence of images as an input, and outputs the corresponding silent password for the frames extracted from the input video. In this paper, three techniques will be employed to extract effective visual features from mouth-lip movement. Such techniques are SURF, HoG and Haar feature extractor. The resultant features in each technique are fed separately into a classification model, namely, the hidden Markov model (HMM). The HMM identifies corresponding Arabic figure from a predefined lexicon based on input features. The final classification models that are produced from the three techniques have been grouped in a voting scheme to produce the final classification result. The proposed model will be tested on handcrafted data set of lip movement, and it has shown a promising result with improved accuracy of Arabic figures recognition. INDEX TERMS Hidden Markov model, lip analysis, silent password, voting scheme.

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Section: Dual Authentication Frameworkmentioning

confidence: 99%

A Silent Password Recognition Framework Based on Lip Analysis

2020

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“…The availability of huge amounts of data, hardware resources and machine learning techniques allow to train computers to derive meaningful information from images. Thus, CSEM can be identified with image classification techniques [ 5 ], and its content can be explored using object detection methods [ 6 , 7 , 8 , 9 ] and object recognition techniques [ 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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“…Thus, multiple objects can be detected and classified within an image with high performance. Image classification techniques allow for the identification of specific content [ 8 ], which can be further explored through object detection [ 9 , 10 , 11 ] and object recognition techniques [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

Blanco‐Medina

Fidalgo

Alegre

et al. 2020

Sensors

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Retrieving text embedded within images is a challenging task in real-world settings. Multiple problems such as low-resolution and the orientation of the text can hinder the extraction of information. These problems are common in environments such as Tor Darknet and Child Sexual Abuse images, where text extraction is crucial in the prevention of illegal activities. In this work, we evaluate eight text recognizers and, to increase the performance of text transcription, we combine these recognizers with rectification networks and super-resolution algorithms. We test our approach on four state-of-the-art and two custom datasets (TOICO-1K and Child Sexual Abuse (CSA)-text, based on text retrieved from Tor Darknet and Child Sexual Exploitation Material, respectively). We obtained a 0.3170 score of correctly recognized words in the TOICO-1K dataset when we combined Deep Convolutional Neural Networks (CNN) and rectification-based recognizers. For the CSA-text dataset, applying resolution enhancements achieved a final score of 0.6960. The highest performance increase was achieved on the ICDAR 2015 dataset, with an improvement of 4.83% when combining the MORAN recognizer and the Residual Dense resolution approach. We conclude that rectification outperforms super-resolution when applied separately, while their combination achieves the best average improvements in the chosen datasets.

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Techniques and Challenges of Face Recognition: A Critical Review

Cited by 93 publications

References 35 publications

A Silent Password Recognition Framework Based on Lip Analysis

A Silent Password Recognition Framework Based on Lip Analysis

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

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