Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications

Louis, Wael; Plataniotis, Konstantinos N.

doi:10.1155/2011/745487

Cited by 17 publications

(12 citation statements)

References 37 publications

(115 reference statements)

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“…. , no of samples} and its class label Y n ∈ {1, -1} is used to find weak classifiers h j (x) [31]. These weak classifiers h j (x) are used to construct a strong classifier H(X).…”

Section: The Proposed Methods Video Based Face Recognizationmentioning

confidence: 99%

Boosting Based Implementationof Biometric Authentication in IoT

Thilagavathi

Suthendran

2018

JCSM

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In security and control application the biometric authentication played a specific and important role to identify the person. Analysis of face recognization is the prerequisite process for the entire authentication. This paper focuses an automatic real-time implementation of face recognization system by highlevel description language such as python. Comparing the biometrics where still images are used, video based biometric holds ample information than a single image. It provides the innovative solution for automatic real time face recognition from the video by the following algorithms like Adaboost, Haar cascade classifier and local binary pattern Histogram (LBPH). From the video stream, the input images are trained by adaboost algorithm which is implemented in cascade classifier. AdaBoost is called as adaptive boosting algorithm. It is a learning algorithm which has been combined with a weak classifier in order to form a strong classifier. Here, the real time testing image is compared with the 627 frames of trained images which are obtained from 47.7 MB (56088648 bytes) video. The hardware implementation of face recognization is obtained and the result can be stored and viewed in http://169.254.108.24. Thus by the above said procedures are authenticated by the combinational performance of Adaboost and Cascade classifier.

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“…. , no of samples} and its class label Y n ∈ {1, -1} is used to find weak classifiers h j (x) [31]. These weak classifiers h j (x) are used to construct a strong classifier H(X).…”

Section: The Proposed Methods Video Based Face Recognizationmentioning

confidence: 99%

Boosting Based Implementationof Biometric Authentication in IoT

Thilagavathi

Suthendran

2018

JCSM

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“…Video detecting category addresses issues regarding to automatic detection of anomalous, forbidden, dangerous events or abandoned object (counting moving people, ship detection, after-the-fact event, intruder detection, trajectory-based unusual behavior detection, motion detection, mult iple moving object detection, face detection, pedestrian detection, vehicle detection, unattended object detection, etc) . Video encoding [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [16], [104] [105], [106], [107], [108], [109], [110], [7], [111] [112], [113], [114], [115] [116],[8], [117] Object detection is performed by co mmon statistical learning techniques with dynamically learning background model of the scene and applies the reference model to find out which section of the scene match with mov ing object. Reasoning refers to generating new explanations, facts and knowledge of dynamic scenes by applying inference engine and method (rule and case based reasoning, Bayesian network, decision tree).…”

Section: Communication Layermentioning

confidence: 99%

Systematic Review and Classification on Video Surveillance Systems

Chamasemani¹,

Affendey

2013

IJITCS

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Abstract-Recently, various conferences and journals have published articles related to Video Surveillance Systems, indicating researchers' attention. The goal of this review is to examine the latest works were published in journals, propose a new classification framework of video surveillance systems and investigate each aspect of this classification framework. This paper provides a co mprehensive and systematic literature review of v ideo surveillance systems from 2010-2011, extracted fro m six online d igital libraries using article's t itle and keyword. The proposed classification framework is expanded on the basis of architecture of v ideo surveillance systems, which is composed of six layers: Concept and Foundation Layer, Network Infrastructure Layer, Processing Layer, Co mmunicat ion Layer, Applicat ion Layer, and User Interaction Layer. Th is review shows , although many publication and research focus on real-time aspect of the challenge, only few researches have investigated the deployment of ext racted and retrieved information for forensic video surveillance.

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“…However, due to the lack of spatial relationships among local textures, there have still serious disadvantages in the original LBP representation. Therein, an extension of the LBP called CoLBP [15,16], has been proposed by considering the co-occurrence (spatial context) among adjacent LBPs, which proved promising performances on several classification applications [6]. In addition, by integrating orientation context, Nosaka et al [17,18] explored a rotation invariant co-occurrence among LBP, which is shown to be more discriminant on image classification compared to CoLBP.…”

Section: Introductionmentioning

confidence: 99%

Co-occurrence context of the data-driven quantized local ternary patterns for visual recognition

Han

Chen

2017

IPSJ T Comput Vis Appl

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In this paper, we describe a novel local descriptor of image texture representation for visual recognition. The image features based on micro-descriptors such as local binary patterns (LBP) and local ternary patterns (LTP) have been very successful in a number of applications including face recognition, object detection, and texture analysis. Instead of binary quantization in LBP, LTP thresholds the differential values between a focused pixel and its neighborhood pixels into three gray levels, which can be explained as the active status (i.e., positively activated, negatively activated, and not activated) of the neighborhood pixels compared to the focused pixel. However, regardless of the magnitude of the focused pixel, the thresholding strategy remains fixed, which would violate the principle of human perception. Therefore, in this study, we design LTP with a data-driven threshold according to Weber's law, a human perception principle; further, our approach incorporates the contexts of spatial and orientation co-occurrences (i.e., co-occurrence context) among adjacent Weber-based local ternary patterns (WLTPs, i.e., data-driven quantized LTPs) for texture representation. The explored WLTP is formulated by adaptively quantizing differential values between neighborhood pixels and the focused pixel as negative or positive stimuli if the normalized differential values are large; otherwise, the stimulus is set to 0. Our approach here is based on the fact that human perception of a distinguished pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. By integrating co-occurrence context information, we further propose a rotation invariant co-occurrence WLTP (RICWLTP) approach to be more discriminant for image representation. In order to validate the efficiency of our proposed strategy, we apply this to three different visual recognition applications including two texture datasets and one food image dataset and prove the promising performance that can be achieved compared with the state-of-the-art approaches.

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Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications

Cited by 17 publications

References 37 publications

Boosting Based Implementationof Biometric Authentication in IoT

Boosting Based Implementationof Biometric Authentication in IoT

Systematic Review and Classification on Video Surveillance Systems

Co-occurrence context of the data-driven quantized local ternary patterns for visual recognition

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