Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm

Seal, Ayan; Gangulyb, Suranjan; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar

doi:10.1016/j.protcy.2012.05.097

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…The present method has been compared with previous works of Seal et al 36,37,39 on thermal face image recognition on the basis of their performances on the UGC-JU face database. In their previous work, three di®erent methods have been used to extract the blood perfusion image, namely bit-plane slicing and medial axis transform, morphological erosion and medial axis transform, `sobel' edge operators.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Robust Thermal Face Recognition Using Region Classifiers

Seal

Bhattacharjee

Nasipuri

et al. 2014

Int. J. Patt. Recogn. Artif. Intell.

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This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 di®erent region classi¯ers. The region classi¯ers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classi¯er is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identi¯cation process di±cult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

“…When only one-sided semipro¯le thermal images of an individual are available, a mosaicking technique can be applied to build an apparent 2D front pro¯le view of that person. 39 In Ref. 13, authors have used Log-Gabor wavelets for IR face recognition.…”

Section: Spectrum (Lwirmentioning

confidence: 99%

Robust Thermal Face Recognition Using Region Classifiers

Seal

Bhattacharjee

Nasipuri

et al. 2014

Int. J. Patt. Recogn. Artif. Intell.

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“…Ayan, Suranjan, Debotosh, Mita and Dipak Kumar proposed the use of Thermal Minutiae Points (TMP), using blood perfusion data as a feature, for thermal face recognition. They experimented with PCA, LDA, and Equinox, for feature extraction and a multilayer perceptron back propagation feed-forward neural network for feature classification and recognition of thermal infrared face images [32]. Siu-Yeung, Lingyu, and Wen proposed, using the Modified Hausdorff Distance, to measure the similarity between two feature vectors of thermal faces [33].…”

Section: Shangfei Et Al Worked On a Natural Visible And Thermalmentioning

confidence: 99%

Multi Biometric Thermal Face Recognition Using FWT and LDA Feature Extraction Methods with RBM DBN and FFNN Classifier Algorithms

Hurwitz¹,

Orji²

2019

Adv. sci. technol. eng. syst. j.

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Person recognition using thermal imaging, multi-biometric traits, with groups of feature filters and classifiers, is the subject of this paper. These were used to tackle the problems of biometric systems, such as a change in illumination and spoof attacks. Using a combination of, hard and soft-biometric, attributes in thermal facial images. The hardbiometric trait, of the shape of a head, was combined with soft-biometric traits such as the face wearing glasses, face wearing a cap/headgear, face with facial hairs, plain face, female face, and male face. These were experimented with, using images from Carl's database and Terravic Facial Infrared Database, and used to train clusters of neural network algorithms for each biometric trait. These comprised Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), and Feed Forward Neural Networks (FFNN). After feature extraction, using Fast Wavelet Transform (FWT), and Linear Discriminant Analysis (LDA). A classification error of 0

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“…In the presence of 2D thermal face image, this problem can be solved. In thermal face recognition process, minutia points are extracted from blood perfusion data [10]. Thus, different kinds of minutia points are extracted and recognized.…”

Section: Depth Analysismentioning

confidence: 99%

Depth based Occlusion Detection and Localization from 3D Face Image

Ganguly¹,

Bhattacharjee²,

Nasipuri

2015

IJIGSP

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Abstract-In this paper, authors have proposed two novel techniques for occlusion detection and then localization of the occluded section from a given 3D face image if occlusion is present. For both of these methods, at first, the 2.5D or range face images are created from input 3D face images. Then for detecting the occluded faces, two approaches have been followed, namely: block based and threshold based. These two methods have been investigated individually on Bosphorus database for localization of occluded portion. Bosphorus database consists of different types of occlusions, which have been considered during our research work. If 2D and 3D images are compared then 3D images provide more reliable, accurate, valid information within digitized data. In case of 2D images each point, named as pixel, is represented by a single value. One byte for gray scale and three byte for color images in a 2D grid whereas in case of 3D, there is no concept of 2D grid. Each point is represented by three values, namely X, Y and Z. The 'Z' value in X-Y plane does not contain the reflected light energy like 2D images. The facial surface's depth data is included in Z's point set. The threshold or cutoff based technique can detect the occluded faces with the accuracy 91.79% and second approach i.e. block based approach can successfully detect the same with the success rate of 99.71%. The accuracy of the proposed occlusion detection scheme has been measured as a qualitative parameter based on subjective fidelity criteria.

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Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm

Cited by 9 publications

References 14 publications

Robust Thermal Face Recognition Using Region Classifiers

Robust Thermal Face Recognition Using Region Classifiers

Multi Biometric Thermal Face Recognition Using FWT and LDA Feature Extraction Methods with RBM DBN and FFNN Classifier Algorithms

Depth based Occlusion Detection and Localization from 3D Face Image

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