Classifying expressions by cascade-correlation neural network

The classification of facial expressions by cascade-correlation neural networks [1] is described. A success rate of 100% over the training data for each of six categories of emotion —happiness, sadness, anger, surprise, fear and disgust — and of up to 87.5% over the same categories for the test data, has been achieved. By using single emotion nets for each category, together with a Net for Resolution, the results represent a 12.5% success rate beyond what was achieved by a single net classifying over all six emotion categories. Face data in the form of 10 hand measurements made on 94 well validated full face photographs [2] provided the input data after normalisation. These measures, among others, had previously been shown to discriminate between emotions [3].

[1]  Fumio Hara,et al.  Recognition of Mixed Facial Expressions by Neural Network. , 1993 .

[2]  H. Yamada.  Visual Information for Categorizing Facial Expression of Emotions , 1993 .

[3]  Giancarlo Mauri,et al.  A multiple network architecture for face recognition , 1991 .

[4]  P. Ekman,et al.  Smiles when lying. , 1988, Journal of personality and social psychology.

[5]  P. Ekman.  Unmasking The Face , 1975 .

[6]  Brian D. Ripley,et al.  Statistical aspects of neural networks , 1993 .

[7]  Toru Yamaguchi,et al.  Conceptual Fuzzy Sets Application to Facial Expression Recognition using Associative Memory System , 1993 .

[8]  Fumio Hara,et al.  Dynamic recognition of basic facial expressions by discrete-time recurrent neural network , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[9]  I. Pilowsky,et al.  Towards the quantification of facial expressions with the use of a mathematic model of the face , 1986 .

[10]  David Renshaw,et al.  Automatic face recognition , 1992 .

[11]  Emmanuel Viennet,et al.  Solving the Human Face Recognition Task using Neural Nets , 1992 .

[12]  Xiaoguang Jia,et al.  On developing an extended feature set for automatic face recognition , 1992 .

[13]  I. Craw.  Recognising face features and faces , 1992 .

[14]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[15]  W. J. Welsh.  Model-based coding of videophone images , 1991 .

[16]  Hiroshi Harashima,et al.  A facial motion synthesis for intelligent man-machine interface , 1991, Systems and Computers in Japan.

[17]  Tsunehiro Aibara,et al.  Human face profile recognition by a P-Fourier descriptor , 1993 .

[18]  Jun S. Huang,et al.  Human face profile recognition by computer , 1990, Pattern Recognit..

[19]  Janet Wiles,et al.  Operators and Curried Functions: Training and Analysis of Simple Recurrent Networks , 1991, NIPS.

[20]  David Renshaw,et al.  IEE Colloquium `Machine storage recognition of faces' , 1992 .

[21]  D. J. Myers,et al.  Automatic location of visual features by a system of multilayered perceptrons , 1992 .

[22]  Alex Pentland,et al.  Face Processing: Models For Recognition , 1990, Other Conferences.

[23]  M. Katsikitis,et al.  The classification of facial emotions: a computer-based taxonomic approach. , 1994, Journal of affective disorders.

[24]  Gaile G. Gordon,et al.  Application of morphology to feature extraction for face recognition , 1992, Electronic Imaging.

[25]  P. Ekman.  Pictures of Facial Affect , 1976 .

[26]  Osamu Nakamura,et al.  Model-based coding for facial images based on feature points and isodensity maps. , 1991 .

[27]  P. Ekman,et al.  Unmasking the Face: A Guide to Recognizing Emotions From Facial Expressions , 1975 .

[28]  G. Kearney,et al.  Machine Interpretation of Emotion: Design of a Memory‐Based Expert System for Interpreting Facial Expressions in Terms of Signaled Emotions , 1993 .

[29]  P. Ekman,et al.  The Duchenne smile: emotional expression and brain physiology. II. , 1990, Journal of personality and social psychology.

[30]  P. Ekman,et al.  Facial action coding system: a technique for the measurement of facial movement , 1978 .