Book ChapterDOI
Thermal IR Face Recognition Using Zernike Moments and Multi Layer Perceptron Neural Network (MLPNN) Classifier
Vijayalakshmi G. V. Mahesh,Alex Noel Joseph Raj,Pachiyappan Arulmozhivarman +2 more
- pp 213-222
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TLDR
The proposed method shows that the combination of magnitudes of ZM obtained from orders zero to two as feature vector provides the best average recognition accuracy of 89.5% and false acceptance rate of 0.356%.Abstract:
Infrared (IR) face recognition is getting wide attention with its increased number of applications as it provides numerous advantages over visual face recognition. As IR images are invariant to different illumination conditions they can provide robust thermal characteristics. The paper proposes a thermal IR based face recognition system using Zernike moments ZM and Multi Layer Perceptron Neural Network. The recognition experiment was performed using the images obtained from Terravic Facial IR Database with variations in poses (front, left and right) and environments (indoor/outdoor). The proposed method shows that the combination of magnitudes of ZM obtained from orders zero to two as feature vector provides the best average recognition accuracy of 89.5% and false acceptance rate of 0.356%.read more
Citations
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A Near-infrared Image Based Face Recognition System.
TL;DR: Both face and facial feature localization and face recognition are performed using local features with AdaBoost learning and the system achieves excellent accuracy, speed and usability.
Journal ArticleDOI
Efficient thermal face recognition method using optimized curvelet features for biometric authentication
TL;DR: In this article , the authors proposed a thermal face-based biometric authentication system which comprises five phases: a) capturing the user's face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, feature extraction using wavelet and curvelet transform, feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB).
Proceedings ArticleDOI
A New Hybrid Shape Moment Invariant Techniques for Face Identification in Thermal and Visible Visions
TL;DR: In this article, the authors investigated three different moment invariants techniques for robust facial features extraction and then determine how each one of these moments is affected by whether the face image was thermal or on a greyscale with the proposal of a hybrid technique that dealt with the robust descriptors of each method.
Proceedings ArticleDOI
Two-Directional Two-Dimensional PCA: An Efficient Face Recognition Method for Thermal Infrared Images
TL;DR: Wang et al. as mentioned in this paper proposed a thermal infrared face recognition method based on the two-directional two-dimensional PCA (2D2DPCA) and random forest classifier.
References
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Journal ArticleDOI
Face recognition: A literature survey
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Journal ArticleDOI
Human and machine recognition of faces: a survey
TL;DR: A critical survey of existing literature on human and machine recognition of faces is presented, followed by a brief overview of the literature on face recognition in the psychophysics community and a detailed overview of move than 20 years of research done in the engineering community.
Journal ArticleDOI
Invariant image recognition by Zernike moments
Alireza Khotanzad,Y.H. Hong +1 more
TL;DR: A systematic reconstruction-based method for deciding the highest-order ZERNike moments required in a classification problem is developed and the superiority of Zernike moment features over regular moments and moment invariants was experimentally verified.
Book
Discovering Knowledge in Data: An Introduction to Data Mining
TL;DR: The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.