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The Man By Paul Ekman Analysis

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Paul Ekman is the man who studied intensively emotions and the connection that emotions have to facial expressions. In what follows we will provide hopefully a better understanding of these emotions to the reader and try to explain how facial features are modified from one emotion to another; how the shape of the lips change, how the eyes tend to be wide open in some emotions or the person to have a relaxed look, if the person has wrinkles or not, the shape of the brows also has a significant importance in determining the type of emotion, mouth may be left open or closed in a tensed and firm position. The description of these emotions is suggested by Ekman and Friesen [2] in their research over the years.
The six types of emotions that will …show more content…
Happiness is the most wanted emotion by all humans. A happy person may be described as a person wearing a smile. Having a smile may include showing teeth or not, but the corners of the lips are creating the form of a half moon and their cheeks are raised. Having a smile on the face may show some wrinkles below the eyes and in their corner and also the eyes may be raised but this does not include a starring, or a tensed look, but rather a happy one. Sadness is the opposite of happiness. Unfortunately many people nowadays are feeling sad. These kinds of humans have a look on their faces that makes the skin under the eyes to form the shape of a triangle and also the lips, as for the happy people, create the shape of a half moon, but this half-moon is rotated in such way to have an opposite direction to the direction of the half-moon from the happy people’s faces. The lips may be also trembling if the emotion is accentuated and the inner corner of the lip situated above the lower lip is being …show more content…
The facial feature extraction in this case (for still images) is divided into two approaches: holistic approach and local approach. The holistic approach relates to the concept of wholes, concept of a complete system that cannot be taken by components, cannot be dissected. For this approach the feature extraction can be done using Principal Component Analysis (PCA) algorithm, which we will use and later on describe it and make it more understandable for the reader. Feature extraction can also be done using Gabor wavelet. In this specific approach, Gabor wavelet is used in the training and recognition part. After the face is detected, using Gabor wavelet and then PCA algorithm we can have a facial recognition. The Gabor filters are very helpful in the extraction of features from images and they have applications in image processing (for example fingerprint recognition, eye

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