Face Recognition Based Person Specific Identification for Video Surveillance Applications

Kokila, S.; Yogameena, B.

doi:10.1145/2791405.2791454

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…Goldmann [35] and Monari [49] used a pixel wise method proposed by Horpraser et al [50] to detect persons in video streams. This algorithm closely mimics the human vision system in which the sensitivity to illumination is more than the sensitivity to the colour.…”

Section: Methods Used For Person Re‐identificationmentioning

confidence: 99%

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Review of person re‐identification techniques

Saghafi

Hussain

Zaman

et al. 2014

IET Computer Vision

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Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.

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Section: Methods Used For Person Re‐identificationmentioning

confidence: 99%

“…Co‐occurrence matrices also have been used for texture description [35]. This descriptor is constructed from squared matrices, which provide information about the neighbouring pixels’ relativity.…”

Section: Methods Used For Person Re‐identificationmentioning

confidence: 99%

Review of person re‐identification techniques

Saghafi

Hussain

Zaman

et al. 2014

IET Computer Vision

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“…Harguess et al [66] proposed a face recognition system that tackles the problem of self-occlusion by observing a person from multiple cameras with different views of the person's face and fusing the recognition results. Kokila and Yogameena [67] present a face detection and recognition algorithm that identify a wanted person in a surveillance video based on the Viola-Jones algorithm. A Histogram of Oriented Gradients (HOG) and LBP features are extracted from the segmented face.…”

Section: Occlusionmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people's head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research.

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“…Feature extraction algorithms used in detecting the face are implemented in a wide range of real time, non-real time and android face applications [14]. The realization of various face detection algorithms has paved way to the exponential rise in the development of numerous face related applications such as authentication system [2], surveillance system [3], emotion recognition[7], [8], iris detection system [38]], speech production application [6], automated attendance system [1] driver fatigue detection [14] etc. Some of the common face detection algorithms used in these applications include; Support Vector Machine (SVM), local Binary Pattern (LPB), Ada boost, Eigen faces, template matching, neural networks, Viola Jones, Principle Component Analysis (PCA) [5], [11] and [12].…”

Section: Categories Of Face Detection Feature Extraction Algorithmsmentioning

confidence: 99%

“…According to [3], [16], [15] and [13] [13] discussed on "Facial parts detection using Viola-Jones Cascade Object Detector" using a combination of filters and methods to detect face, eyes, nose and mouth of a human faces. The Bao database with varying pose and light was used to test the classifier compared to AR and Yale face datasets which comprises less complex invariant frontal face images.…”

Section: Fig 4 Ada Boost Classifiermentioning

confidence: 99%

Exploration of Face Detection methods in Digital Images

Ruvinga*,

Malathi,

Jayaseeli

2019

IJRTE

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Face detection is a challenging computer vision task that identifies and localizes the faces of human beings from digital images or video streams. It is predominantly the first phase in the process of developing a wide range of face applications such as face recognition, emotion recognition, authentication, surveillance systems etc. The process of face detection is easy from the human perspective but, a complex task for computers that involves searching of the face in variable circumstances of pose, colour, size, occlusion, illumination etc. If the outcome of face detection is intended to be input for another algorithm, an accurate, well informed selection of an appropriate face detection technique is essential because the overall performance of face application is dependent on face detection algorithm’s precision. The survey paper presents a review of three commonly used face detection algorithms available in literature namely Viola Jones, Neural networks (NN) and Local Binary Pattern (LBP) for the purpose of ascertaining the most suitable face detection algorithm to implement for our future work in developing an ‘Online student concentration level recognition system’.

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Face Recognition Based Person Specific Identification for Video Surveillance Applications

Cited by 9 publications

References 16 publications

Review of person re‐identification techniques

Review of person re‐identification techniques

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Exploration of Face Detection methods in Digital Images

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