Deep Perusal of Human Face Recognition Algorithms from Facial Snapshots

Dubey, Deepika; Tomar, Geetam Singh

doi:10.14257/ijsip.2016.9.9.10

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…Multi-directional face image information can be extracted by Gabor wavelets. The features extracted by Gabor filters are called Gabor features, and these features exist in local areas with multiple scales [10]. Because Gabor filters create redundancy and this affects face recognition, an algorithm was introduced in which, instead of using Gabor filters alone, a combination of Gabor filters calculated using PCA (Principal Component Analysis) is used.…”

Section: Gabor and Pca Algorithmsmentioning

confidence: 99%

Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms

Kamyab

Delrish

Daealhaq

et al. 2022

IJRCS

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The face plays an essential role in identifying people and showing their emotions in society. The human ability to recognize faces is remarkable. But face recognition is a fundamental problem in many computer programs. Due to the inherent complexities of the face and the many changes in its features, different algorithms for face recognition have been introduced in the last 20 years. Face recognition methods that are based on the structure of the face are unsupervised methods that produce good results compared to the linear changes that occur in the image. In this article, the Gabor algorithm, which is the origin of face recognition algorithms, has been described. Over the past decade, most of the research in the area of pattern classification has emphasized the use of the Gabor filter bank for extracting features. Because the Gabor algorithm has shortcomings, researchers have introduced a new method that is a combination of Gabor and PCA. After the introduction of the Gabor method, more complete and accurate algorithms have been introduced, such as Boosting algorithms, which we have briefly explained in this article. Also, here are the results of the comparison made by the researchers between Boosting and Gabor algorithms. The results show that Boosting-based algorithms have performed better compared to Gabor-based algorithms.

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Section: Gabor and Pca Algorithmsmentioning

confidence: 99%

Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms

Kamyab

Delrish

Daealhaq

et al. 2022

IJRCS

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“…To get the Facial Expressions, Feature Points plays major role.This is used to form a Geometric Graph. Feature Based Techniques easily manage the variations in scale, size, and head orientation of an image but, it is more expensive than the Template Based Techniques [6].…”

Section: Review Of Literaturementioning

confidence: 99%

Facial Expression Recognition Based on Feature Extraction

Goyal¹,

Goyal²,

Jadon³

2017

IJNSE

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“…Another serious problem occurred when the facial expression of the same face is to be changed in between input image and stored image. Even a smile or sadness effect the face detection system [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Echelon Based Pose Generalization of Facial Images Approaches

Dubey¹

2017

APJCRI

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Face recognition having many applications applied different in security, using in access control, applied on gaming, also effect by aging effect, health issues, and internet communication and so on. It has been perform on different images either they are stored in database or a new image is captured and take it as input image. Face recognition algorithms having different fields of application. The intelligent image (face) recognition system consists of various components through which human identification and recognition has been done. In real world practical applications unconstrained conditions like illumination and create variations, occlusion and expressions are to be faced by the users. in this paper discuss about the pose invariant technology also study the challenges during face recognition under unconstrained conditions like illumination and cause variations, basically it proposes several possible pose invariant technologies future directions which is excluded from the challenges used.

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Deep Perusal of Human Face Recognition Algorithms from Facial Snapshots

Abstract: Abstract

Cited by 10 publications

References 18 publications

Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms

Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms

Facial Expression Recognition Based on Feature Extraction

Echelon Based Pose Generalization of Facial Images Approaches

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