Expression-invariant facial identification

Tsai, Pohsiang; Tran, Tich Phuoc; Cao, Longbing

doi:10.1109/icsmc.2009.5346008

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…In Figure 8, the result obtained from the investigated work is compared with other techniques. The recognition rates of SVD, LLE-Eigen, FLD-PCA-ANN [13] and method in [8] are 92.96%, 93.93%, 84.9 and 96.6% respectively whereas for the proposed method the recognition rate is 97.3% which outperforms all the other methods discussed. Similarly for the CK database, neutral image is chosen as the reference image per person and rest of the images are used for testing purpose.…”

Section: Comparison With State-of-art Algorithmsmentioning

confidence: 79%

“…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%

“…Inversing the matrix L on to another matrix , which is defined as , which is a column vector of length where is defined as any n-vector . We derive a vector of n weights as and the coefficients of affine transformation as shown in equation (8).…”

Section: Warping Towards Intermediate Meshmentioning

confidence: 99%

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Expression Invariant Face Recognition System

Ahlawat¹,

Nehra²

2017

IJSIP

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Section: Comparison With State-of-art Algorithmsmentioning

confidence: 79%

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

confidence: 99%

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Expression Invariant Face Recognition System

Ahlawat¹,

Nehra²

2017

IJSIP

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“…As AdaBoost algorithm used in classification stage, the result increased to 96%. Pseudo neutral images of the converted facial expression were close to realistic images but does not look like a realistic face image [12].…”

Section: Related Workmentioning

confidence: 91%

“…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%

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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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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Expression-invariant facial identification

Cited by 7 publications

References 18 publications

Expression Invariant Face Recognition System

Expression Invariant Face Recognition System

Frame Work for Expression Invariant Face Recognition System Using Warping Technique

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

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