Hierarchical Fusion of Multi Spectral Face Images for Improved ... - IIIT

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and short-wave IR are due to the reflective properties. In fusion, these properties of shortwaveIR are fused with the properties of visible image to obtain better performance.4.3 Comparison of Proposed Algorithm with Existing Fusion AlgorithmsExperiments are also carried out to compare the performance of the proposed image andfeature fusion algorithm with other existing fusion algorithms. These include imagefusion using concatenation by Chang [6], feature fusion using concatenation technique byRoss [21] (referred as Ross1), and feature and match score fusion by Ross [21] (referredas Ross2). The dataset for this comparison consists of short-wave IR and visible faceimages from the Equinox database. For every individual, one face image from eachspectrum is used for training and rest of the images is used for testing.3025Ross1Ross2ChangProposedFalse Rejection Rate(%)201510500 5 10 15 20 25 30False Accept Rate(%)Fig. 11. ROC plot comparing the performance of proposed algorithm with fusionalgorithms28
During training and testing, we fuse the information from visible and SWIR face imageswhich have similar expression, occlusion and lighting variations. Fig. 11 shows the ROCcurves for the existing fusion algorithms along with the proposed image and featurefusion algorithm. Equal error rate for fusion technique by Chang is 17.63%, by Ross1 is14.10%, by Ross2 is 9.41% and with the proposed algorithm is 2.86%.5 ConclusionIn this paper, we proposed algorithms to fuse the information from multi spectral faceimages. Fusion is performed at both the image and feature level to generate a fusedfeature vector which is used for recognizing a face image. DWT based image fusionalgorithm is proposed to fuse the information from multi spectral face images at imagelevel. Feature fusion algorithm uses a learning algorithm based on SVM whichintelligently selects and fuses good features from two feature sets. A hierarchicalcombination of the two fusion algorithms is then used to generate a fused feature vector.Features extracted from face image are the amplitude and phase features obtained using2D log polar Gabor wavelet. The algorithms are evaluated using the Equinox multispectral face database which consists of face images from thermal IR, reflective IR andvisible spectrum. The experiments are performed using single training image to simulatethe scenario when limited number of images are available for training. Experimentalresults show that the combination of short-wave IR and visible face images performs bestin comparison to other combinations with an EER of 2.86% and is much better than theperformance of face recognition using only visible images. Experimental results also29
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Page 6 and 7: Inverse Fourier Transform (IFT) of
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Page 16 and 17: Since the visible and IR imaging de
Page 18 and 19: DWTLLLHHLHHVisibleFace ImageImageFu
Page 20 and 21: Short Wave IRFace ImageAmplitudeFea
Page 22 and 23: Figs. 8 and 9 show the Receiver-Ope
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Page 26 and 27: Experiments for validating the fusi
Page 30 and 31: show that the proposed image and fe
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Hierarchical Fusion of Multi Spectral Face Images for Improved ... - IIIT

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