Facial expression recognition based on image feature

FACIAL EXPRESSION RECOGNITION BASED ON

IMAGE FEATURE

COMPUTER SCIENCE & ENGINEERING KHULNA UNIVERSITY

ANWESHA PAULID: 110206

TASNIM TARANNUMID: 110216

PRESENTED BY:

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Overview


Facial Expression & Facial Expression Recognition Related Works Problems Of Existing System Motivation Proposed Method Conclusion Reference

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Facial Expression


Powerful, natural and immediate means for human to communicate their emotions.

Vital part of communication.

Widely recognized in social interaction.

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Facial Expression contd…


Neutral Happy Surprise Sad Disgust Angry Fear

Fig 1: Basic Facial Expression.

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Expression Recognition


Locating faces in the scene. Extracting facial features.

Analyzing the motion of facial feature.

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Expression Recognition contd…


Face Acquisition

Facial Data Extraction &

Representation

Facial Expression Recognition

Face Detection

Head Pose

Estimation

Feature-based

Appearance-based

Frame-based

Sequence-based

Fig 2: Basic Structure of Facial Expression Recognition.

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Related Work


Reference Image Acquisition

Feature Extraction Classification Recognition

Performance

Neeta Sarode et al.[1]

Gray scale image to recognize four expressions

2D appearance-based local approach

Euclidean distance

Accuracy rate 81%

Rupinder Saini et al.[2]

PCA, Gabor wavelet, PCA with SVD

Euclidean distance, PCA

[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No. 05, 2010.

[2] Rupinder Saini, Narinder Rana, “Facial Expression Recognition Techniques, Database & Classifiers”, International Journal of Advances in Computer Science and Communication Engineering (IJACSCE), Vol. 2, Issue 2, June 2014.

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Related Work contd..




Performance

Jeemoni Kalita et al.[3]

60 samples with various expression of RGB color image

Manually extracted and Eigenvector based distributed feature

Euclidean distance

Recognition rate 95% & process time 0.0295 sec

Ajit P.Gosavi et al.[4]

Real database image to recognize five basic emotions

PCA (Principal Component analysis) with SVD (Singular Value Decomposition)

Euclidean distance

Avg. accuracy 89.70% & avg. recognition rate 65.42%

[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013.[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with Singular Value Decomposition”, International Journal of Advance Research in Computer Science and Management Studies Vol. 1, Issue 6, November 2013.

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Related Work contd..




Performance

Akshat Garget et al.[7]

Gray scale image

PCA (Principal Component analysis)

Euclidean distance & PCA

Accuracy rate 89.0%

Mahesh Kumbhar et al.[8]

JAFFE [6] database image

PCA(Principal Component analysis), Gabor wavelet

Euclidean distance

Recognition rate 60% to 70%

[

7] Akshat Garg, Vishakha Choudhary, “Facial Expression Recognition Using Principal Component Analysis”, International Journal of Scientific Research Engineering &Technology (IJSRET), Vol. 1 Issue4, July 2012.

[8] Mahesh Kumbhar, Ashish Jadhav, Manasi Patil, “Facial Expression Recognition Based on Image Feature”, International Journal of Computer and Communication Engineering, Vol. 1, No. 2, July 2012.

[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http:// www.kasrl.org/jaffe.html

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Problems Of Existing System


Don’t contain enough feature points

PCA-based face recognition systems are hard to scale up

Color image burdensome[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No. 05, 2010.

[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with Singular Value Decomposition”, International Journal of Advance Research in Computer Science and Management Studies Vol. 1, Issue 6, November 2013.

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Problems Of Existing System contd…


Cropping manually is time killing

Inabilities (different angles and different reasons).

[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013.

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Motivation


Recognize facial expression as like a human.

Recognize six basic expressions.

Increase the accuracy rate.

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Proposed Method


Block diagram of proposed system:

Image Acquisition

Feature Extraction

Classifier

Happy SadSurpriseAngryFearDisgust

Fig 3: Block diagram.

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Proposed Method contd….


Image Acquisition: Convert the color image into gray scale

image.

Fig 4: RGB- color image converted into gray scale

image.

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Proposed Method contd…


Feature Extraction:• Gaussian filter.

• Radial Symmetry Transform.

Fig 5: application of Gaussian filter.

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Feature Extraction:

• Edge projection.

• Segmentation using Laplacian of Gaussian operator at zero threshold.

.

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Classifier:• Euclidean distance based on

geometrical relationship.

• The feature vector V.

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Classifier: Here, Vd0 = distance of eyebrow,Vd1 = distance between right eyebrow and nose tip,Vd2 = distance between left eyebrow and nose,Vw = mouth width,Vh = mouth height,Vul = upper lip curvature,Vll = lower lip curvature.

Fig 6: Geometrical parameters of the face, forming the

feature vector.

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Decision Making Techniques:

• Feature vector calculation.

• Observe each component of feature vector.

• Comparison between testing image and neutral image.

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Result Analysis:

Comparison between the testing image with it’s corresponding images from training database [6].

[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http://

www.kasrl.org/jaffe.html

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Conclusion


Recognize six basic facial expressions.

Future work: Will develop the same in real time videos.

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Reference


[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No. 05, 2010.

[2] Rupinder Saini, Narinder Rana, “Facial Expression Recognition Techniques, Database & Classifiers”, International Journal of Advances in Computer Science and Communication Engineering (IJACSCE), Vol. 2, Issue 2, June 2014.

[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013.

[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with Singular Value Decomposition”, International Journal of Advance Research in Computer Science and Management Studies Vol. 1, Issue 6, November 2013.

[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http:// www.kasrl.org/jaffe.html

[7] Akshat Garg, Vishakha Choudhary, “Facial Expression Recognition Using Principal Component Analysis”, International Journal of Scientific Research Engineering &Technology (IJSRET), Vol.

1 Issue4, July 2012.

[8] Mahesh Kumbhar, Ashish Jadhav, Manasi Patil, “Facial Expression Recognition Based on Image Feature”, International Journal of Computer and Communication Engineering, Vol. 1, No. 2, July 2012.


Thank you.

Facial expression recognition based on image feature

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