In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the use of the gradient and the Laplacian of the input image together with the original input into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, the plain CNN is not able to extract this information from the raw images. We have performed a number of experiments on two well known datasets KDEF and FERplus. Our approach enhances the already high performance of state-of-the-art FER systems by 3 to 5 .
@cite_28: Abstract : Research relevant to psychotherapy regarding facial expression and body movement, has shown that the kind of information which can be gleaned from the patients words - information about affects, attitudes, interpersonal styles, psychodynamics - can also be derived from his concomitant nonverbal behavior. The study explores the interaction situation, and considers how within deception interactions differences in neuroanatomy and cultural influences combine to produce specific types of body movements and facial expressions which escape efforts to deceive and emerge as leakage or deception clues.
@cite_12: A device for use with a sewing machine using bobbins to determine either when a predetermined amount of bobbin thread remains on the bobbin or, alternatively, when the bobbin is completely empty. The device employs a probe which is inserted into the bobbin when the sewing machine is not operating and will thereafter produce a signal if the bobbin is low on thread or empty.
@cite_3: Acknowledgments List of Illustrations Figures Plates Preface to the Anniversary Edition by Paul Ekman Preface to the Third Edition by Paul Ekman Preface to the Second Edition by Francis Darwin Introduction to the Third Edition by Paul Ekman The Expression of the Emotions in Man and Animals Introduction to the First Edition 1. General Principles of Expression 2. General Principles of Expression -- continued 3. General Principles of Expression -- continued 4. Means of Expression in Animals 5. Special Expressions of Animals 6. Special Expressions of Man: Suffering and Weeping 7. Low Spirits, Anxiety, Grief, Dejection, Despair 8. Joy, High Spirits, Love, Tender Feelings, Devotion 9. Reflection - Meditation - Ill-temper - Sulkiness - Determination 10. Hatred and Anger 11. Disdain - Contempt - Disgust - Guilt - Pride, Etc. - Helplessness - Patience - Affirmation and Negation 12. Surprise - Astonishment - Fear - Horror 13. Self-attention - Shame - Shyness - Modesty: Blushing 14. Concluding Remarks and Summary Afterword, by Paul Ekman APPENDIX I: Charles Darwin's Obituary, by T. H. Huxley APPENDIX II: Changes to the Text, by Paul Ekman APPENDIX III: Photography and The Expression of the Emotions, by Phillip Prodger APPENDIX IV: A Note on the Orientation of the Plates, by Phillip Prodger and Paul Ekman APPENDIX V: Concordance of Illustrations, by Phillip Prodger APPENDIX VI: List of Head Words from the Index to the First Edition NOTES NOTES TO THE COMMENTARIES INDEX
@cite_6: 
Darwin suggested that human and animal facial emotions are evolutionary @cite_3 . Motivated by Darwin's work, @cite_28 @cite_12 found that the seven expressions, namely happiness, anger, fear, surprise, disgust, sadness and contempt remain the same across different cultures. Facial action coding system (FACS) is proposed in @cite_6 to investigate the facial expressions and the corresponding emotions described by the activity of the atomic action units (cluster of facial muscles). Facial expression can be analyzed by mapping facial action units for each part of the face (eyes, nose, mouth corners) into codes.