This document presents a facial expression recognition method using facial movement features. It proposes extracting patch-based features and defining a patch matching operation to build distance features. It evaluates the approach on the JAFFE and CK face databases, comparing results of four distance metrics. Experimental results show the Dense L2 distance metric performs best on both databases, and the JAFFE database benefits from larger patch sizes than the CK database.

1. Facial Expression Recognition
Using Facial
Movement Features
Ligang Zhang, Student Member,
IEEE, and Dian Tjondronegoro
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, VOL. 2,
NO. 4,
OCTOBER-DECEMBER 2011
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2. Presented by
Taslima Akter
Dept. of CSE
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3. Overview
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4. Proposed work
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5. Page 5

6. Building Distance Features
 Patch-Based Feature Extraction
 Patch Matching Operation
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7. Patch-Based Feature Extraction
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8. Patch Matching Operation
 Matching Area and Matching Scale
Definition
 Distance Metric Definition
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9. Matching Area and Matching
Scale Definition
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10. Distance Metric Definition
 Dense L1(DL1)
 Dense L2(DL2)
 Sparse L1 (SL1)
 Sparse L2 (SL2)
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11. Databases
 JAFFE(Japanese Female Facial
Expression)
 CK(Cohn-Kanade AU coded facial
Expression)
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12. Experimental Results
• JAFFE
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13. Experimental Results
• JAFFE Database
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14. Experimental Results
• CK Database
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15. Experimental Results
• CK Database
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16. Comparison with State-of
the-Art Performance
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17. Conclusion
• Show better performances
• For both the JAFFE and CK databases,
• DL2 performs the best among four
distances.
• The JAFFE database requires larger sizes
of patches than the CK database.
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18. Thank You 
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