Face recognition improvement by converting expression faces to neutral faces

Petpairote, Chayanut; Madarasmi, Suthep

doi:10.1109/iscit.2013.6645898

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Petpairote and Madarsami proposed Thin Plate Spline Warping (TPSW) [14] method to warp expression faces to neutral faces with a significant improvement in recognition accuracy. The experimental results for face recognition of MUG-FED and AR-Face databases showed a significant improvement in recognition rate for Principal Component Analysis(PCA) [15], Linear Discriminate Analysis (LDA) [16,17] and Local Binary Pattern(LBP) [18] methods after warping the expression face to neutral face.…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers have investigated methods to improve the face recognition by removing the facial expressions to obtain a neutral face i.e. making the face expression-invariant [10][11][12][13][14][15]. Hence to develop a robust face recognition algorithm which is insensitive to expression variations is one of the greatest challenges in this field.…”

Section: Introductionmentioning

confidence: 99%

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Frame Work for Expression Invariant Face Recognition System Using Warping Technique

Ahlawat¹,

Bpsmv²,

Nehra³

et al. 2018

IJIGSP

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Facial expressions, usually has an adverse effect on the performance of a face recognition system. In this investigation, expression invariant face recognition algorithm is presented that converts input face image with an arbitrary expression into its corresponding neutral facial image. In the present study, deep learning algorithm is used to train classifiers for reference keypoints, where key-points are located and deep neural network is trained to make the system able to locate the landmarks in test image. Create an intermediate triangular mesh from the test and reference image and then warp it using affine transform and take the average of the normalized faces. To extract the features presented in the result image shift invariant feature extraction technique is used. Finally, results are compared and the recognition accuracy is determined for different expressions. The present work is tested on three different databases: JAFFE, Cohn-Kanade (CK) and Yale database. Experimental results show that the expression invariant face recognition method is very robust to variety of expressions and recognition accuracy is found to be 97.8 %, 96.8% and 95.7% for CK, JAFFE and Yale databases respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frame Work for Expression Invariant Face Recognition System Using Warping Technique

Ahlawat¹,

Bpsmv²,

Nehra³

et al. 2018

IJIGSP

View full text Add to dashboard Cite

show abstract

“…Many researchers have investigated methods to improve the face recognition by removing the facial expressions to obtain a neutral face i.e. making the face expression-invariant [6][7][8][9][10][11][12][13]. Hence to develop a robust face recognition algorithm which is insensitive to expression variations is one of the greatest challenges in this field.…”

Section: Figure 1 Typical Applications Of Face Recognition [4]mentioning

confidence: 99%

Expression Invariant Face Recognition System

Ahlawat¹,

Nehra²

2017

IJSIP

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“…This works but its drawback is the computational cost. Later, Petpairote and Madarasmi [34] presented warping a facial expression to a neutral face reference to create synthesised neutral face by using applied double‐thin‐plate‐splines algorithm. Their work involved a high computational time in the warping process.…”

Section: Introductionmentioning

confidence: 99%

“…Previous research work related to identifying faces, neutralising and recognising facial expressions [3–36] can be divided into two groups: three‐dimensional (3D) and 2D approaches. In the 3D approach [3–7], the methods theoretically improve accuracy but the computational cost is high due to the large amount of data involved.…”

Section: Introductionmentioning

confidence: 99%

Personalised‐face neutralisation using best‐matched face shape with a neutral‐face database

Petpairote

Madarasmi

Chamnongthai

2017

IET Computer Vision

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Conventional personalised-face neutralisation methods use facial-expression databases; however, the database creation and maintenance is a tedious process, and should be minimised. Moreover, face-shape template should be also considerably used due to its crucial factor. This study proposes a personalised-face neutralisation method using best-matched face-shape template with neutral-face database. In personalised-face neutralisation, the best-matched face-shape template which is assumed as the most similar to the neutralisation expression face is found based on coarse-to-fine concept, and used for warping textures. Additionally, closed eyes are detected and opened up by using eye shape of the best-matched face shape, and mixed intensities of original closed-eye and the best-matched one. To evaluate the performance of the proposed method, experiments were performed using the CMU Multi-PIE database and the results reveal that the proposed method reduces gradient mean square error 0.07% on average and improves face recognition accuracy by 1.13% approximately comparing with the conventional method, while requiring only a single neutral database without expression images.

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Face recognition improvement by converting expression faces to neutral faces

Cited by 10 publications

References 13 publications

Frame Work for Expression Invariant Face Recognition System Using Warping Technique

Frame Work for Expression Invariant Face Recognition System Using Warping Technique

Expression Invariant Face Recognition System

Personalised‐face neutralisation using best‐matched face shape with a neutral‐face database

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