237 research outputs found

    Face Recognition Under Varying Illumination

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    This study is a result of a successful joint-venture with my adviser Prof. Dr. Muhittin Gökmen. I am thankful to him for his continuous assistance on preparing this project. Special thanks to the assistants of the Computer Vision Laboratory for their steady support and help in many topics related with the project

    Illumination Processing in Face Recognition

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    Performance analysis of different matrix decomposition methods on face recognition

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    Applications using face biometric are ubiquitous in various domains. We propose an efficient method using Discrete Wavelet Transform (DWT), Extended Directional Binary codes (EDBC), three matrix decompositions and Singular Value Decomposition (SVD) for face recognition. The combined effect of Schur, Hessenberg and QR matrix decompositions are utilized with existing algorithm. The discrimination power between two different persons is justified using Average Overall Deviation (AOD) parameter. Fused EDBC and SVD features are considered for performance calculation. City-block and Euclidean Distance (ED) measure is used for matching. Performance is improved on YALE, GTAV and ORL face databases compared with existing methods

    Performance analysis of different matrix decomposition methods on face recognition

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    Infrared face recognition: a comprehensive review of methodologies and databases

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    Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

    Wavelet and Multiscale Methods

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    Various scientific models demand finer and finer resolutions of relevant features. Paradoxically, increasing computational power serves to even heighten this demand. Namely, the wealth of available data itself becomes a major obstruction. Extracting essential information from complex structures and developing rigorous models to quantify the quality of information leads to tasks that are not tractable by standard numerical techniques. The last decade has seen the emergence of several new computational methodologies to address this situation. Their common features are the nonlinearity of the solution methods as well as the ability of separating solution characteristics living on different length scales. Perhaps the most prominent examples lie in multigrid methods and adaptive grid solvers for partial differential equations. These have substantially advanced the frontiers of computability for certain problem classes in numerical analysis. Other highly visible examples are: regression techniques in nonparametric statistical estimation, the design of universal estimators in the context of mathematical learning theory and machine learning; the investigation of greedy algorithms in complexity theory, compression techniques and encoding in signal and image processing; the solution of global operator equations through the compression of fully populated matrices arising from boundary integral equations with the aid of multipole expansions and hierarchical matrices; attacking problems in high spatial dimensions by sparse grid or hyperbolic wavelet concepts. This workshop proposed to deepen the understanding of the underlying mathematical concepts that drive this new evolution of computation and to promote the exchange of ideas emerging in various disciplines

    Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

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    Use of multi-scale phase-based methods to determine optical flow in dynamic scene analysis

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    Estimates of optical flow in images can be made by applying a complex periodic transform to the images and tracking the movement of points of constant phase in the complex output. This approach however suffers from the problem that filters of large width give information only about broad scale image features, whilst those of small spatial extent (high resolution) cannot track fast motion, which causes a feature to move a distance that is large compared to the filter-size. A method is presented in which the flow is measured at different scales, using a series of complex filters of decreasing width. The largest filter is used to give a large scale flow estimate at each image point. Estimates at smaller scales are then carried out by using the previous result as an a priori estimate. Rather than comparing the same region in different images in order to estimate flow, the regions to be compared are displaced from one another by an amount given by the most recent previous flow estimate. This results in an estimate of flow relative to the earlier estimate. The two estimates are then added together to give a new estimate of the absolute displacement. The process is repeated at successively smaller scales. The method can therefore detect small local velocity variations superimposed on the broad scale flow, even where the magnitude of the absolute displacement is larger than the scope of the smaller filters. Without the assistance of the earlier estimates in ‘tuning\u27 the smaller filters in this manner, a smaller filter could fail to capture these velocity variations, because the absolute displacement carry the feature out of range of-the filter during successive frames. The output of the method is a series of scale-dependent flow fields corresponding to different scales, reflecting the fact that motion in the real world is a scale-dependent quantity. Application of the method to some 1 dimensional test images gives good results, with realistic flow values that could be used as an aid to segmentation. Some synthetic 2-dimentional images containing only a small number of well defined features aIso yield good-results but the method performs poorly on a random-dot stereogram and on a real-world test image pair selected from the Hamburg Taxi sequence

    Effect of cooking time on physical properties of almond milk-based lemak cili api gravy

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    One of the crucial elements in developing or reformulating product is to maintain the quality throughout its entire shelf life. This study aims to determine the effect of different cooking time on the almond milk-based of lemak cili api gravy. Various cooking times of 5, 10, 15, 20, 25 and 30 minutes were employed to the almond milk-based lemak cili api gravy followed by determination of their effects on physical properties such as total soluble solids content, pH and colour. pH was determined by using a pH meter. Refractometer was used to evaluate the total soluble solids content of almond milk-based lemak cili api gravy. The colours were determined by using spectrophotometer which expressed as L*, a* and b* values. Results showed that almond milk-based lemak cili api gravy has constant values of total soluble solids with pH range of 5 to 6, which can be classified as low acid food. Colour analysis showed that the lightness (L*) and yellowness (b*) are significantly increased while redness (a*) decreased. In conclusion, this study shows that physical properties of almond milk-based lemak cili api gravy changes by increasing the cooking time
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