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

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max⎪⎧ 1⎨ −⎪⎩ 2∑( αi j yiy j Ki ) α αi ,i,j⎪⎫( x x ) ⎬ ⎪⎭jwhere,0 ≤ α ≤ C(18)∑i∑iα yiα ≥ υiiii= 0i , j ∈ 1, ...,n and the kernel function isK( x x ) = ϕ ( x ) ϕ ( x )i, (19)jij2υ -SVM is initialized and optimized using iterative decomposition training [9], whichleads to reduced complexity of 2υ -SVM. If the size of data vectors is n, then thecomplexity without optimization is O(n 2 ) and with optimization is O(n) [9]. In thissection, we consider the fusion of phase and amplitude features extracted from a faceimage. The proposed fusion algorithm, shown in Fig. 2, is described as follows:Amplitude FeaturesFace ImageSVMLearningbasedFeatureFusionFused FeatureVectorPhase FeaturesFig. 2. Block diagram for feature level fusion12
Let amplitude features F A and phase features F P be extracted from a face image. Both thefeatures are divided into windows of size 3 x 3 to calculate the weighted average windowbased activity. A total of n activity levels are computed and provided as input to the 2υ -SVM which is then trained to determine whether the coefficient from the amplitudefeature set F A or the phase feature set F P should be used. At any position (x, y), if thefeatures of F A are classified as “good” and the SVM classification margin of F A is greaterthan the SVM classification margin of F P , then output O(x, y) of the learning algorithm is1 otherwise the output is -1. As shown in Equation 20, amplitude and phase features areselected depending on the output O ( x,y).⎧FAFF(x,y)= ⎨⎩FP( x,y)( x,y),,ififO(x,y)> 0O(x,y)< 0(20)where FF is the fused feature vector. Furthermore, to match the two fused feature vectors,FF1and FF 2, the correlation based matching technique is applied. The features are firstdivided into m frames, each of size k x l. The correlation distance, CDiF, between twocorresponding frames is computed using Equation 21. Using the frame matchingthresholdT IF , the intermediate matching score MS IF for the frames and the finalmatching scoreMS F is calculated using Equation 22. A person is said to be matchedif MS F is greater than the fused feature vector matching threshold, T F .CDiFiiFF FF=1 ⊗ 2 , i = 1 ,2,…,m(21)k l13
Page 4 and 5: face is detected using triangle bas
Page 6 and 7: Inverse Fourier Transform (IFT) of
Page 8 and 9: whereiPF 1 andiFP2are the i th fram
Page 10 and 11: training data is used, it could lea
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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