LBP based on multi wavelet sub-bands feature extraction used for face recognition

The strategy of extracting discriminant features from a face image is immensely important to accurate face recognition. This paper proposes a feature extraction algorithm based on wavelets and local binary patterns (LBPs). The proposed method decomposes a face image into multiple sub-bands of frequencies using wavelet transform. Each sub-band in the wavelet domain is divided into non-overlapping sub-regions. Then LBP histograms based on the traditional 8-neighbour sampling points are extracted from the approximation sub-band, whilst 4-neighbour sampling points are used to extract LBPHs from detail sub-bands. Finally, all LBPHs are concatenated into a single feature histogram to effectively represent the face image. Euclidean distance is used to measure the similarity of different feature histograms and the final recognition is performed by the nearest-neighbour classifier. The above strategy was tested on two publicly available face databases (Yale and ORL) using different scenarios and different combination of sub-bands. Results show that the proposed method outperforms the traditional LBP based features

Face Recognition using Multi Region Prominent LBP Representation

Various face recognition methods are derived using local features among them the Local Binary Pattern (LBP) approach is very famous. The histogram techniques based on LBP is a complex task. Later Uniform Local Binary Pattern (ULBP) is derived on LBP, based on the bitwise transitions and ULBP’s are treated as the fundamental property of texture. The ULBP approach treated all Non-Uniform Local Binary Patterns’ (NULBP) into one miscellaneous label. Recently we have derived Prominent LBP (PLBP), Maximum PLBP (MPLBP) and Smallest PLBP (SPLBP). The PLBP consists of the majority of the ULBP’s and some of the NULBP’s. The basic disadvantage of these various variants of LBP’s  is they are basically local approaches and completely failed in representing features derived from large regions or macrostructures, which are very much essential for faces. This paper derives PLBP’s on the large region. The rectangular region of this paper is assumed with a size of multiples of three and PLBPs are evaluated on dividing each region into multiple regions. The proposed Multi Region-PLBP (MR-PLBP) approach is tested on three facial databases namely Yale, Indian and AT&T ORL. The experimental results show the proposed approach significantly outperforms the other LBP based face recognition methods

A Biological Hierarchical Model Based Underwater Moving Object Detection

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

AUTOMATIC ASSESSMENT MARK ENTRY SYSTEM USING LOCAL BINARY PATTERN (LBP)

Offline handwritten recognition continues to be a fundamental research problem in document analysis and retrieval. The common method used in extracting handwritten mark from assessment forms is to assign a person to manually type in the marks into a spreadsheet. This method is found to be very time consuming, not cost effective and prone to human mistakes. In this project, a number recognition system is developed using local binary pattern (LBP) technique to extract and convert students’ identity numbers and handwritten marks on assessment forms into a spreadsheet. The template of the score sheet is designed as in Appendix 1 to collect sample of handwritten numbers. The training data contain three sets of LBP histograms for each digit. The recognition rate of handwritten digits using LBP is about 50% because LBP could not fully describe the structure of the digits. Instead, LBP is useful in term of arranging the digits ‘0 to 9’ from highest similarity score to the lowest similarity score as compared to sample using chi square distance. The recognition rate is greatly improved to about 95% by verifying the output of chi square distance with the salient structural features of digits

AUTOMATIC ASSESSMENT MARK ENTRY SYSTEM USING LOCAL BINARY PATTERN (LBP)

Offline handwritten recognition continues to be a fundamental research problem in document analysis and retrieval. The common method used in extracting handwritten mark from assessment forms is to assign a person to manually type in the marks into a spreadsheet. This method is found to be very time consuming, not cost effective and prone to human mistakes. In this project, a number recognition system is developed using local binary pattern (LBP) technique to extract and convert students’ identity numbers and handwritten marks on assessment forms into a spreadsheet. The template of the score sheet is designed as in Appendix 1 to collect sample of handwritten numbers. The training data contain three sets of LBP histograms for each digit. The recognition rate of handwritten digits using LBP is about 50% because LBP could not fully describe the structure of the digits. Instead, LBP is useful in term of arranging the digits ‘0 to 9’ from highest similarity score to the lowest similarity score as compared to sample using chi square distance. The recognition rate is greatly improved to about 95% by verifying the output of chi square distance with the salient structural features of digits

Elaborazione dell'immagine del palmo della mano per il riconoscimento di identità

Questo lavoro di tesi tratta dell’elaborazione dell’immagine del palmo della mano ai fini dell’autenticazione biometrica. L’immagine della parte centrale del palmo, estratta nella fase di preprocessing, viene suddivisa in aree non sovrapposte allo scopo di utilizzarne solo alcune nella fase di riconoscimento. Per l’estrazione delle caratteristiche viene utilizzato il filtro di Gabor, che permette di estrarre le tessiture (texture) dell’immagine. Vengono testate diverse situazioni, in base alla scelta delle porzioni dell’immagine da elaborare, e queste saranno confrontate con la soluzione realizzata in letteratura, senza suddivisione dell’immagin