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

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whereiPF 1 andiFP2are the i th frames for the two phase templates, andiHDPis thecorresponding hamming distance measure.c) The phase matching score MS P is calculated using Equation 13,MSIP⎪⎧MS= ⎨⎪⎩ MSIPIP+ 1ififHDHDiPiP≤ T> TIPIP(13)MSP =MSmIPwhere T IP is the frame matching threshold, MS IP is the intermediate phase matchingscore, and m is the number of frames.d) A match occurs if the phase matching score, MS PthresholdT P ., is greater than the matchingThe advantage of the proposed face recognition algorithm is that the amplitude and phasefeatures extracted from the filter response of face image are invariant to frequency, scaleand orientation of the filter. Here we transform the problem of recognizing a face into anefficient test of statistical dependence operating on amplitude variation and statisticalindependence operating on phase variation. Also, Venkatesh and Owens [26] show thatthe phase is independent of the overall magnitude of face image and provides invarianceto the changes in illumination and contrast. Log polar transformation eliminates thenecessity of using multiple templates to handle the variations due to scale and rotation.Thus the amplitude features in the proposed face recognition algorithm are invariant tofrequency, scale, and orientation; while the phase features are invariant to frequency,scale, orientation, illumination and contrast.8
3 Fusion at Image level and Feature Level for Enhanced PerformanceIn [20], it has been suggested that biometric information fusion can be used to enhancethe recognition performance. Information fusion can be performed at five levels namelydata or image level, feature level, match score level, decision level, and rank level.Researchers have proposed several algorithms [5], [14], [20] for fusion at match scorelevel and decision level. Kumar [16], Ross [21], and Singh [23] proposed algorithms forfeature level fusion, and Chang [6] and Singh [23] proposed algorithms for image levelfusion. In this section, we present algorithms for both feature and image level fusion inface biometrics. The two levels of fusion are further combined hierarchically to increasethe recognition performance.3.1 Fusion of Features from Single Spectrum Face ImageFeature fusion in biometrics has been addressed by several researchers [21], [23] in theliterature. In [21], feature fusion is performed by concatenating two feature vectors.Singh et al. [23] fuse the eigen vectors of visible and IR face image using geneticalgorithms. In the proposed approach, a learning based algorithm is used to fuse theamplitude and phase features from a face image. There are several learning algorithmsbased on neural networks which can be trained to learn and identify features. However,one of the major drawbacks with neural network based approach is choosing a mappingfunction between the input data space and the output feature space. The mapping functionmay be inefficient in determining the local minima consistently. When a large set of9
Page 4 and 5: face is detected using triangle bas
Page 6 and 7: Inverse Fourier Transform (IFT) of
Page 10 and 11: training data is used, it could lea
Page 12 and 13: max⎪⎧ 1⎨ −⎪⎩ 2∑( αi
Page 14 and 15: MSF⎪⎧MS= ⎨⎪⎩ MSIFIFMS+ 1F
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
Page 24 and 25: In specific applications such as la
Page 26 and 27: Experiments for validating the fusi
Page 28 and 29: and short-wave IR are due to the re
Page 30 and 31: show that the proposed image and fe
Page 32: [19] F. Prokoski, History, Current

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

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