Slide 1

A Synopsis of the proposed research plan submitted for the degree ofMaster of TechnologyinComputer Science and Engineering

PRESENTED BY:-AKHIL UPADHYAYM-TECH 3rd SEM CSEROLL NO.- 121140002GUIDED BY:-MR. ROHIT MIRIH.O.D. OF COMPUTER SCIENCE DEPARTMENT

PERFORMANCE EVALUATION AND IMPLEMENTATION OF FACIAL EXPRESSION AND EMOTION RECOGNITION SYSTEM USING PRINCIPAL COMPONENT ANALYSIS

1.INTRODUCTION Human correspondence has two primary viewpoints: Verbal (sound-related) Non-verbal (visual)

Facial Expression is the most important mode of non-verbal communication between people.

Facial expression carries crucial informationabout the mental, emotional and even physical states of the conversation.

(Cont..)

The human face is an elastic object that consists of organs, numerous muscles, skins, and bones. When a muscle contracts, the transformation of the corresponding skin area attached to the muscle result in a certain type of visual effect.

Facial expression is one of the most powerful, natural, and immediate means for human beings to communicate their emotions and intentions.

The face can express emotion soonerthan people verbalize or even realize their feelings.

The contours of the mouth, eyes andeyebrows play an important role in classification.

(Cont..)

HUMAN EMOTIONS Humans are filed with numerous type of expression and emotions. But the universal categories of emotions are happiness, sadness, surprise, fear, anger, and disgust.

Happiness The power of an upbeat expression is basically dictated by the lip position. The mouth might possibly be pated, with teeth uncovered or not. Furthermore, a glad individual shows wrinkle lines running from the nose out and down to the range past the edges of the mouth.

6

Anger The eyebrows are brought down are brought down, and drawn together, the eyelids are tensed, and drawn together, the eyelids are tensed, and the eye seems to gaze in hard manner. The lips are either firmly squeezed together or opened in a square.

7

Surprise broadening of the eyes, brief suspension of breathing, and general loss of muscle tone. The loss of muscle tone causes the mouth to fall open, and may make the subject amaze or compel him to take a seat. Practically anything can be astounding; a sight, smell, taste, touch and definitely, the more prominent the unforeseen thing, the more amazed you will be.

8

Disgust The presence of full-face revulsion is portrayed as; the upper top is raised, the lower lip is raised and pushed up to the upper lip, or is brought down and the nose is wrinkled, the cheeks are raised, lines show beneath the lower top pushed up however not strained, the forehead is brought down, bringing down the upper top.

9

Fear The presence of full-face apprehension is portrayed as; the eyebrows are raised and drawn together, the eyes are open, and the lower cover is tensed, and lips are extended back dissimilar to in astonishment look.

10

Sadness The internal corners of the eyebrows are drawn up and the upper eyelid is raised and the edge of the lips are down. Misery mixes with trepidation and outrage generally.

11

LITERATURE REVIEWA Robust Face Detection Method Based on Skin Color and EdgesDeepak Ghimire et al. (2013) Face detection is concerned with finding whether or not a face exists in a given image; if face is present, it returns the image location and face size.

The advance of computer technology in recent years has facilitated the development of real-time vision modules that can interact with humans.

Facial Expression Recognition using Neural Network Pushpaja V. Saudagare et al. (2012) in many face recognition systems the important part is face detection. Classification of face detection and token matching can be carried out any neural network for recognizing the facial expression

OBJECTIVE Facial expression recognition system has turn into a most emphasizing research area since it plays a most important part in human-computer-interaction.

The face can articulate emotion sooner than people verbalize or even understand their posture.

This synopsis report describes Integrated System for Facial Expression Recognition (ISFER), which performs facial expression analysis from a still dual facial view image. Modeling the facial emotion and its intensity.

METHODOLOGY Principal Component Analysis (PCA) is a classical feature extraction and data representation technique widely used in the area of pattern recognition and Computer Vision.

The Purpose of PCA is to reduce the large dimensionality data space into the smaller dimensionality feature space need to describe the data economically

The main idea of PCA is to find the vectors which best accounts for the distributions of face images within the entire image space.

14

PROPOSED WORK Image AcquisitionPreprocessingSegmentationExtractionClassificationFig: Face recognition system can be formulated as following phases Image Acquisition :

Images used for facial expression recognition are still images or image sequence.

Two Dimensional facial images are used

Two Dimensional human facial images pixel and intensities are used as input dataset

15

PROPOSED WORK Image AcquisitionPreprocessingSegmentationExtractionClassificationFig: Face recognition system can be formulated as following phases Preprocessing :

Preprocessing of acquired image is important step to get efficient result.

It includes noise removal, and normalization against the variation of pixel position or brightness.

PCA technique is used for normalization and resizing of image pixel in N*N Matrix

16

PROPOSED WORK Image AcquisitionPreprocessingSegmentationFeature ExtractionClassificationFig: Face recognition system can be formulated as following phases Segmentation :

Segmentation is concerned with the demarcationof image portions conveying relevant facial information

shape , motion, color, texture, and spatial configuration of the face or its Components.

Covariance Matrix of dimensions N2*N2 to determine eigen vectores and eigen values.

17

PROPOSED WORK Image AcquisitionPreprocessingSegmentationFeature ExtractionClassificationFig: Face recognition system can be formulated as following phasesClassification :

Classification technique is used to classify image category.

In this Eigen vectors are compared with the training set to classify the image of different moods.

This is the last process to get desired output.

18

Application With facial expression recognition systems, the computer will be able toassess the human expressions depending on their effective state in the same way that humans senses do

Facial expression recognition has practical significance; it has very broad application prospects, such as user-friendly interface between man and machine, humanistic design of goods, and emotional robot etc

The facial expression recognition system has been used in communication to make the answer machine more interactive with people

the facial expression recognition system has been used in communication to make the answer machine more interactive with people

The facial expression recognition system applied in different areas of life such as security and surveillance, they can predict the offender or criminals behavior by analyzing the images of their faces that are captured by the control-camcorder.

EXPECTED OUTCOMES There has been continued research interest in enabling computer systems to recognize expressions and to use the emotive information embedded in them in human-machine interfaces.

This paper presents a high-level overview of automatic expression recognition; it highlights the main system components and some research challenges.

This work provided a framework for facial expression recognition that can effectively maximize performance

REFERANCE [1]Anjana R., Lavanya M. Facial Emotions Recognition System For Autism International Journal of Advanced Engineering Technology -June,2014 /40-43[2] F. Bourel, C.C. Chibelushi, A.A. Low, "Robust Facial Expression Recognition Using a State-Based Model of Spatially-Localised Facial Dynamics", Proc. Fifth IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 106-111, 2002[3] F. Bourel, Models of Spatially-Localised Facial Dynamics for Robust Expression Recognition, Ph.D. Thesis, Staffordshire University, 2002 [4] V. Bruce, "What the Human Face Tells the Human Mind: Some Challenges for the Robot-Human Interface", Proc. IEEE Int. Workshop Robot and Human Communication, pp. 44-51, 1992