Face Recognition System Using PCA and DCT in HMM

Jameel, S.A.

doi:10.17148/ijarcce.2015.4103

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…• Recognition system using PCA and discrete cosine transform (DCT) in HMM [79]: Without using DCT, PCA is directly used to reduce the dimension. First, the details of the face are taken in blocks, and the DCT is applied on these blocks.…”

Section: Hybrid Approaches and Methods Based On Statistical Modelsmentioning

confidence: 99%

A Survey of 2D Face Recognition Techniques

et al. 2016

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Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method's principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

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Section: Hybrid Approaches and Methods Based On Statistical Modelsmentioning

confidence: 99%

A Survey of 2D Face Recognition Techniques

et al. 2016

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“…Biometrics is the automatic use of physiological or behavioral traits to determine or confirm the identification of a person, the physiological biometrics examines physiological characteristics like, iris, faces, fingerprints, DNA, and hand geometry; the behavioral biometrics examines behavioral issues, such as voice, signature, and gait [1]. The significance of computerized identification of people has accelerated throughout the previous decades, particularly in excessive security areas such as airports and banks [5,7]. Each character has distinguishable unique traits (biometrics) that can be used by identification systems to verify and pick out the person's identity [8].…”

Section: Introductionmentioning

confidence: 99%

Human gait recognition using preprocessing and classification techniques

Jameel¹,

Qadir²,

Ahmed³

2020

IJECE

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Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

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Face recognition under pose and illumination variations using the combination of Information set and PLPP features

Hanmandlu

Singhal

2017

Applied Soft Computing

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Face Recognition System Using PCA and DCT in HMM

Cited by 7 publications

References 29 publications

A Survey of 2D Face Recognition Techniques

A Survey of 2D Face Recognition Techniques

Human gait recognition using preprocessing and classification techniques

Face recognition under pose and illumination variations using the combination of Information set and PLPP features

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