Study On Multi-scale Invariant Features Of Images And Its Applications

Feature extraction is the core technology of pattern recognition and computervision, which is widely concerned by scholars for decades. Since the existing of allkinds of image transformation including translation, rotation, scale, illumination,viewpoint and so on in reality environment, it becomes increasingly important toenhance features invariance to improve the performance of classification andidentificationBased on summarizing the traditional methods, in this dissertation, we study on theimage invariant feature extraction deeply and systematically, focus on a new imageinvariant representation called Scattering transform (SCATT), and apply it to facerecognition and key-point detection. For occlusion face recognition, we propose a newscheme by introducing the scattering transform into the image gradient domain; Takingthe advantage of the characteristics from the second-order scattering coefficients, wepropose a scheme for image key-point detection and, the relating experiments verify theeffectiveness of the scheme; With regard to face recognition under varying lighting, wepresent a new multi-scale illumination invariance using wavelet. In this dissertation, themain work and contributions are as follows:Firstly, a new occlusion robust face recognition scheme called Gradientscatt isproposed based on SCATT. By the idea ‘Recognition by the Rest’, SCATT is appliedinto the image gradient domain. On the one hand, the negative impact on the frequencystructure of the original image due to the occlusion block are greatly suppressed,however, Gradientscatt can fully extracted the information from the parts uncoveredthanks to SCATT’s local translation invariant and elastic deformation stability. It isdifferent from the two kinds of traditional methods--‘Local feature recognition’ and‘Sparse representation classification’, which are always trying to rebuild the occludedparts.‘Local feature recognition’ using the weighted sum of the salient features in theface to characterize the face, which not only needs more training samples and non-zerovalue belonging to non-significant characteristics are produced.‘Sparse representationclassification’ consider the occluded face as the sum of the unobstructed part andocclusion block, which introduce the occlusion dictionary presenting the occlusionblock while unobstructed part should only be represented sparsely by the trainingimages. Face was reconstructed only by the sparse coefficients corresponding to the training image and the coefficients projected on the occlusion dictionary are discarded.There were two problems: Firstly, the occlusion in reality is often non-linear so as to itis impossible to remove the negative impact completely due to occlusion block bythrowing away the occlusion dictionary coefficients; Secondly, training images islimited and sparse representation of limited training image is not sufficient to embracedeformations existing in face. Experimental results show that Gradientscatt is superiorto the above-mentioned two main methods, which obtains a very high recognition ratefor occlusion face recognition.Secondly, we propose an image key-point detector called SCD based on thesecond-order scattering energy. Nowadays popular key-point features are generallydetected in the multi-scale space, such as Harris-Laplace operator and SIFT. However,all of the multi-scale methods to detect key-point face a common problem: the localstructures in image often exist in a scale range instead of a fixed scale. Detecting thekey-point in the multi-scale space will inevitably get many points with similar positionand scale and they all represent the same local structure, undoubtly redundantkey-points would result in a significant increase of mismatch. The second-orderscattering energy is a reflection of structural similarity between the scales. The localextreme points of the second-order scattering energy graph are corresponding tokey-points, which are inter-scale local features, so the stability and criticality of thekey-points is higher than that corresponding one at a single scale only. Experimentalresults show that the number of the key-points got by the SCD method is relatively lessthan Harris and SIFT, and which are more structural and more robust to imageviewpoint changes, scale transformation and projection change.Thirdly, a novel method to extract illumination invariant features is proposed forface recognition under varying lighting conditions called MGF. Varying Lighting is oneof the key problems for face recognition. Due to the illumination change, the facialfeatures are no longer to be distinguished clearly, which cause bigger intra-classdivergence, even greater than the inter-class divergence of face samples. The relatingresearches show that the difference between the images from the same person underdifferent light conditions may be even larger than that from the different people underthe same light conditions. The MGF applies wavelet transform on Gaussian differentialof image to extract the direction as invariants. Theoretical analysis confirms the MGF isan illumination insensitive measure, and which keeps more structure information. MGFprovides an approach to construct more illumination invariant measure using wavelet with more different characteristics. Experimental results indicate that MGF caneffectively improve the robustness to varying lighting for face recognition.