Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Learning, even fails to perform well in these scenarios. We propose a novel soft-margin based learning method for multiple feature-kernel combinations, followed by feature transformed using Domain Adaptation, which outperforms many recent state-of-the-art techniques, when tested using three real-world surveillance face datasets. © 2016 IEEE.

Sukhendu Das

Department of Computer Science and Engineering

Samik Banerjee

Biometrics

Computer vision

Monitoring

Pattern Recognition

BIOMETRIC TRAITS

Deep learning

domain adaptation

ILLUMINATION CONDITIONS

Learning methods

Multiple features

State-of-the-art technology

TRAINING SAMPLE

Face recognition

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Soft-Margin Learning for Multiple Feature-Kernel Combinations with Domain Adaptation, for Recognition in Surveillance Face Datasets

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Learning based on convolutional neural networks (CNNs) or deep learning has been a major research area with applications in face recognition (FR). However, performances of algorithms designed for FR are unsatisfactory when surveillance conditions severely degrade the test probes. The work presented in this paper has three contributions. First, it proposes a novel adaptive-CNN architecture of deep learning refurbished for domain adaptation (DA), to overcome the difference in feature distributions between the gallery and probe samples. The proposed architecture consists of three components: feature (FM), adaptive (AM) and classification (CM) modules. Secondly, a novel 2-stage algorithm for Mutually Exclusive Training (2-MET) based on stochastic gradient descent, has been proposed. The final stage of training in 2-MET freezes the layers of the FM and CM, while updating (tuning) only the parameters of the AM using a few probe (as target) samples. This helps the proposed deep-DA CNN to bridge the disparities in the distributions of the gallery and probe samples, resulting in enhanced domain-invariant representation for efficient deep-DA learning and classification. The third contribution comes from rigorous experimentations performed on three benchmark real-world surveillance face datasets with various kinds of degradations. This reveals the superior performance of the proposed adaptive-CNN architecture with 2-MET training, using Rank-1 recognition rates and ROC and CMC metrics, over many recent state-of-the-art techniques of CNN and DA. © 2018 Gesellschaft fuer Informatik.

2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018

Deep Domain Adaptation for Face Recognition using images captured from surveillance cameras

Learning based on convolutional neural networks (CNNs) or deep learning has been a major research area with applications in face recognition (FR). Under degraded conditions, performance of FR algorithms severely degrade. The work presented in this paper has three contributions. First, it proposes a transfer-CNN architecture of deep learning tailor-made for domain adaptation (DA), to overcome the difference in feature distributions between the gallery and probe samples. The proposed architecture consists of three units: base convolution (BCM), transfer (TM) and linear (LM) modules. Secondly, a novel 3-stage algorithm for Mutually Exclusive Training (3-MET) based on stochastic gradient descent, has been proposed. The initial stage of 3-MET involves updating the parameters of the BCM and LM units using samples from gallery. The second stage involves updating the parameters of TM, to bridge the disparity between the source and target distributions, based on mutual variation of information (MVI). The final stage of training in 3-MET freezes the layers of the BCM and TM, for updating (fine-tuning) only the parameters of the LM using a few probe (as target) samples. This helps the proposed transfer-CNN to provide enhanced domain-invariant representation for efficient deep-DA learning and classification. The third contribution comes from rigorous experimentations performed on three benchmark real-world degraded face datasets captured using surveillance cameras, one real-world dataset with non-uniform motion blur and three synthetically degraded benchmark face datasets. This exhibits superior performance of the proposed transfer-CNN architecture with 3-MET training, using Rank-1 recognition rates and ROC and CMC metrics, over many recent state-of-the-art techniques of CNN and DA. Experiments also include performance analysis under unbiased training with two large-scale chimeric face datasets. © 2018 Elsevier B.V.

Neurocomputing

Mutual variation of information on transfer-CNN for face recognition with degraded probe samples

Face Recognition (FR) is the most accepted method of biometric authentication due to its inherent passive nature. This has attracted a lot of researchers over past few decades to achieve an moderately high accuracy under controlled environments. In order to achieve such an accuracy for FR under surveillance scenario has been proved to be a major hurdle in this area of research, mainly due to the difference in resolution, contrast, illumination and camera parameters of the training and the testing samples. In this paper, we propose a novel technique to find the optimal feature-kernel combination by SML MFKC (Soft-margin Learning for Multi-Feature-Kernel Combination) to solve the problem of FR in surveillance, followed by an Eigen Domain Transformation (EDT) to bridge the gap between the distributions of the gallery and the probe samples. Rigorous experimentation has been performed on three real-world surveillance face datasets: FR SURV [24], SCface [17] and ChokePoint [35]. Results have been shown using Rank-1 Recognition rates, ROC and CMC measures. Our proposed method outperforms all other recent state-of-the-art techniques by a considerable margin. Experimentations also show that the recent state-of-the-art Deep Learning techniques also fail to perform appreciably compared to our proposed method for the afore-mentioned datasets. © Springer International Publishing AG 2017.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Eigen domain transformation for soft-margin multiple feature-kernel learning for surveillance face recognition

This study describes a new technique of unsupervised domain adaptation based on eigenanalysis in kernel space, for the purpose of categorisation tasks. The authors propose a transformation of data in source domain, such that the eigenvectors and eigenvalues of the transformed source domain become similar to that of the target domain. They extend this idea to the reproducing kernel Hilbert space, which enables to deal with non-linear transformation of source domain. They also propose a measure to obtain the appropriate number of eigenvectors needed for transformation. Results on object, video and text categorisations tasks using real-world datasets show that the proposed method produces better results when compared with a few recent state-of-art methods of domain adaptation. © 2015 The Institution of Engineering and Technology.

IET Image Processing

Unsupervised domain adaptation using eigenanalysis in kernel space for categorisation tasks

Face Recognition (FR) in surveillance scenarios has attracted the attention of researchers over the last few years. The bottleneck as a large gap in both resolution and contrast between training (high-resolution gallery) and testing (degraded, low quality probes) sets, must be overcome using efficient statistical learning methods. In this paper, we propose a Bag-of-Words (BOW) based approach for face recognition combined with Domain Adaptation (DA), to overcome this challenging task of FR in degraded conditions. The dictionary of BOW is formed using dense-SIFT features, using an adaptive spatially varying density. The sampling of the keypoints is denser in the discriminative parts of the face, while it is loosely sampled at some less-interesting (pre-decided) zones of the face. FR using BOW-based face representation is made more efficient using an unsupervised method of DA. Proposed method of DA considers the training set to be the source and the test set to be the target domains. Transformation from source to target is estimated using eigen-analysis of the BOW-based features, which is the novelty and contribution of our proposed work on FR for surveillance applications. Results on the two-real world surveillance face datasets shows the efficiency of the proposed method using ROC and CMC measures. Copyright 2014 ACM.

ACM International Conference Proceeding Series

Face Recognition in surveillance conditions with Bag-of-Words, using unsupervised Domain Adaptation

Face images obtained by an outdoor surveillance camera, are often confronted with severe degradations (e.g., low-resolution, low-contrast, blur and noise). This significantly limits the performance of face recognition (FR) systems. This paper presents a framework to overcome the degradation in images obtained by an outdoor surveillance camera, to improve the performance of FR. We have defined a measure that is based on the difference in intensity histograms of face images, to estimate the amount of degradation. In the past, super-resolution techniques have been proposed to increase the image resolution for face recognition. In this work, we attempt a combination of partial restoration (using super-resolution, interpolation etc.) of probe samples (long distance shots of outdoor) and simulated degradation of gallery samples (indoor shots). Due to the unavailability of any benchmark face database with gallery and probe images, we have built our own database and conducted experiments on a realistic surveillance face database. PCA and FLDA have been used as baseline face recognition classifiers. The aim is to illustrate the effectiveness of our proposed method of compensating the degradation in surveillance data, rather than designing a specific classifier space suited for degraded test probes. The efficiency of the method is shown by improvement in the face classification accuracy, while comparing results obtained separately using training with acquired indoor gallery samples and then testing with the outdoor probes. © 2011 Springer-Verlag.

Face recognition on low quality surveillance images, by compensating degradation

Procedings of the British Machine Vision Conference 2015

Deep Face Recognition

IEEE Transactions on Image Processing

Multiple Kernel Learning for Sparse Representation-Based Classification

Journal	Data powered by TypesetIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publisher	Data powered by TypesetIEEE Computer Society
ISSN	21607508
Open Access	No