Recent Advances on Singlemodal and Multimodal Face Recognition: A Survey

Zhou, Hongyuan; Mian, Ajmal; Wei, Lei; Creighton, Doug; Hossny, Mohammed; Nahavandi, Saeid

doi:10.1109/thms.2014.2340578

Cited by 84 publications

(41 citation statements)

References 97 publications

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“…Former studies have shown that, when a large number of representative training images are available, computer algorithms are able to recognize even better than humans [6], [7], [25], [27], [33]. These algorithms, before recognizing, represent the face in the feature space, and perhaps because they are able to extract information from training images about the changes caused by different conditions they outperform humans in recognition [25].…”

Section: Introductionmentioning

confidence: 99%

Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification

Haamer

Kulkarni

Imanpour

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Abstract-Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.

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Section: Introductionmentioning

confidence: 99%

Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification

Haamer

Kulkarni

Imanpour

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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“…In the last few decades the pattern recognition, biometrics and feature extraction became very popular research area among the researchers [1]- [4]. In this context, the face recognition problem can be viewed as a classification problem where one or more test images are compared with the face images stored in the databases.…”

Section: Introductionmentioning

confidence: 99%

“…methods can be classified into three main categories: (a) holistic approach, (b) feature based approach and (c) hybrid approach [1]- [9].…”

Section: Introductionmentioning

confidence: 99%

Weighted Fuzzy Generalized 2DFLD: A Fuzzy-Based Feature Extraction Technique for Face Recognition

Dey¹,

Sing²,

Chowdhury³

2017

IJMLC

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Abstract-This paper proposes a weighted scheme and fuzzy logic-based feature extraction technique, called weighted fuzzy generalized two-dimensional Fisher's linear discriminant (WFG-2DFLD) and its use for face recognition using radial basis function (RBF) neural network as a classifier. In particular, the WFG-2DFLD method is extended version of the generalized two-dimensional Fisher's linear discriminant (G-2DFLD) method. Like G-2DFLD, WFG-2DFLD also maximizes class separability along row and column directions simultaneously. Firstly, it calculates fuzzy membership matrix by fuzzy k-nearest neighbour (Fk-NN) algorithm for the training samples. Secondly, the fuzzy membership values are combined with the training samples to obtained global mean and class-wise mean training images. Thereafter, the global and class-wise mean images are used to generate fuzzy within-class and fuzzy between-class scatter matrices along the row and column directions. In order to make more accurate for classification, different weights are incorporated to scatter matrices. Finally, by solving the Eigen value problems of these scatter matrices; we find the optimal fuzzy projection vectors, which actually used to generate more discriminant features for face recognition. The WFG-2DFLD method has been evaluated on the YALE, AT&T (formally known as ORL), UMIST and FERET face databases using RBF neural network. Simulation results demonstrate that the proposed WFG-2DFLD method can obtain higher recognition rates than some state-of-the-art face recognition methods.Index Terms-WFG-2DFLD, fuzzy projection vector, Fk-NN, RBFNN based classifier, matrix-based feature extraction. I. INTRODUCTIONIn the last few decades the pattern recognition, biometrics and feature extraction became very popular research area among the researchers [1]- [4]. In this context, the face recognition problem can be viewed as a classification problem where one or more test images are compared with the face images stored in the databases. Although the face recognition techniques from still images having controlled background have matured substantially, the face recognition task is still very challenging under uncontrolled environment and different issues related to human face i.e. facial expression (unhappiness, happiness) and facial pose, occlusion (wearing beards, mustaches and glasses), illumination variance, etc. Broadly, the face recognition Manuscript received August 21, 2017; revised November 10, 2017. Aniruddha Dey and Jamuna Kanta Sing are with the Department of Computer Science Engineering, Jadavpur University, Kolkata, jksing@ieee.org).Shiladitya Chowdhury is with the Department of Computer Application, Techno India, Kolkata, India (e-mail: dityashila@yahoo.com).methods can be classified into three main categories: (a) holistic approach, (b) feature based approach and (c) hybrid approach [1]- [9].Holistic approach: This approach uses all the information available in the face images as a whole. The subspace-based methods, like principal component analysis (PCA), l...

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“…al. [6] says that the performance of face recognition systems degrades with the variants like illumination, pose, and facial expressions. In their paper, they proposed 3D face recognition systems which contains 3D facial geometry information.…”

Section: Introductionmentioning

confidence: 99%