Exploring Various Aspects of Gabor Filter in Classifying Facial Expression

Parameswaran, Seetha; Parameswaran, Murali; Joseph, Shelbi; Viswanathan, Daleesha M.

doi:10.1007/978-981-15-3992-3_41

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…In this context, SVM is used here for identifying real faces from spoofed ones, as well as separating authorized from unauthorized faces.LBP and Gabor features are used to train the classifier kernel, which can be linear or non-linear. Among its kernels, the Radial Basis Function (RBF) kernel is adopted in the proposed approach, outperforming other kernel types and confirming the results of previous researches like the ones in [23,24].…”

Section: Face Image Classificationsupporting

confidence: 78%

“…This kernel is swept along all pixels of the image and for each pixel, the direct neighboring pixels are thresholded against a certain number then the binary results are grouped together (from left to right and from up to down) to form on number whose decimal equivalent will be put as the new center pixel as shown below [11].The resulted values after applying the kernel on all the pixels in the face image will have better descriptive elements than the original one. Spatial aspect ratio Table 1: Definition the parameters of Gabor filter [22] After preparing the filter's coefficients (x,y,λ,θ,ψ,σ and γ) a feature vector is obtained by convolving the cropped face image with the filter as in the equation below [23]:…”

Section: A Linear Binary Pattern (Lbp)mentioning

confidence: 99%

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Face spoofing detection and Authentication Using Linear Binary Patterns, Gabor Features and Support Vector Machine

Alkababji¹,

Alhabib²,

Al-Dabagh³

2022

4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022)

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Face detection and authentication have become an active field of research material in recent years duo to the increased use of face dependent access control systems, which can be considered as a good alternative to other biometric features such as fingerprint duo to its easily accessible, non- intrusive nature that makes it very important during the ongoing pandemic years. However, this method doesn’t come without security risks related to adversaries seeking to gain unauthorized access to the system. Face spoofing is a security breach attempt occurs when the attacker tries to deceive the face-enabled access control system by displaying a photo, video or wearing a mask of an authorized person to gain access. The paper in hand suggests a method for face anti-spoofing by utilizing some of the well-known feature extraction techniques usually associated with face detection and recognition, namely the LBP and Gabor features, in addition to the commonly used SVM classifier for identifying real and spoofed feces. The algorithm is implemented successfully on multiple individuals, achieving performance levels comparable to other accredited methods in terms of FAR, FRR and HTER verification criteria, aspiring for more advancedand effective methods.

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Section: Face Image Classificationsupporting

confidence: 78%

Section: A Linear Binary Pattern (Lbp)mentioning

confidence: 99%

Face spoofing detection and Authentication Using Linear Binary Patterns, Gabor Features and Support Vector Machine

Alkababji¹,

Alhabib²,

Al-Dabagh³

2022

4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022)

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show abstract

“…For example, the playing board, number in the player's jersey, subtitles on the display, etc. The text of that sort could be extracted with OCR [18] [19]. The text is derived from voice through voice recognition throughout the second category.…”

Section: Text Based Approachmentioning

confidence: 99%