Facial expression recognition android application
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BEB801 Project. Facial expression recognition android application. Student: Alexander Fernicola. Supervisor: Professor Vinod Chandran. Describes the first steps of building an android application that can detect the users facial expression in an image or in video.
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Facial expression
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Data Mining - Facial Expression Recognition
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Emotion Detection Using Facial Expression Recognition to Assist the Visually ...
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DC_1
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Facial Expression Detection for video sequences using local feature extractio...
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static image analysis or dynamic temporal 3D image or video analysis.
The former involves static images taken on an individual at a specific
point in time and is in 2-dimensional format. The latter involves dynamic textures extraction of video sequences extended in a temporal
domain. Dynamic texture analysis involves short term facial expression
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algorithms which are resistant to gray-scale modifications and computation. It is also crucial to note that these emotions being natural reactions, recognition of feature selection and edge detection from the video
sequences can increase accuracy and reduce the error rate. This can be
achieved by removing unimportant information from the facial images.
The results showed better percentage recognition rate by using local facial extraction algorithms like local binary patterns and local directional
patterns than holistic algorithms like GLCM and Linear Discriminant
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Facial expression recognition android application
- 1. FACIAL EXPRESSION RECOGNITION APP BEB801- SEMESTER 1 STUDENT: ALEXANDER FERNICOLA N8609373 SUPERVISOR: VINOD CHANDRAN
- 2. PURPOSE OF PROJECT • To design an android application, that is compatible with most models, specifically android OS 4.4 (KitKat) or later, that can recognise the users facial expression in a photo or a video. • Accuracy goal is a 50-70% positive recognition score (due to lack of resources to train the model with a large enough data set)
- 3. WHY IS THIS PROJECT NEEDED & PRIOR WORK • Advances in facial expression recognition (FER) will lead to advances in affective computing and human computing. • Modern studies in the field of automatic facial expression recognition emerged in 2009. • Major advances in face detection and feature extraction, for example the Viola-Jones classifier and local binary pattern (LBP) feature extraction algorithm.
- 4. BACKGROUND INFO ON MACHINE LEARNING • Process of creating a learned mathematical model to predict output. • (x,y) coordinates of points are the inputs and colours are outputs. • The model is the line that separates the points. • Model is the equation; y = mx + b, where m is the gradient and b is the bias. • Optimization is training the model to match the output of the model with the desired output. • There are many training algorithms such as support vector machines
- 5. PROJECT PLAN
- 6. FACE DETECTION & TRACKING • Face detected by Viola-Jones classifier • The viola jones classifier is a haar like feature cascade classifier that uses the adaboost feature selection algorithm to boost efficiency. • Face tracker used in this project is the Chehra face tracker. 49 fiducial points (green asterisks) are placed on the face so that local texture features can be extracted from around each point. • It is important to track the movements of these points as
- 7. RESEARCH FINDINGS • Table II shows the accuracy of the combinations of feature extraction and selection algorithms. • Feature selection on second column, feature extraction on first row. • Most accurate are LBP+FAP (facial action points) and mRMR (minimal Redundancy Maximum Relevance criterion). • Exclusively LBP is by far the fastest feature extraction algorithm • (L. Zhang et al, Discovering the Best Feature Extraction and Selection Algorithms for Spontaneous Facial Expression Recognition (2012). 2012 IEEE International Conference on Multimedia and Expo
- 8. LOCAL BINARY PATTERN (LBP) FEATURE EXTRACTION • Compares intensity of 8 neighbouring pixels with intensity of centre pixel (threshold value). • Pixels are compared within the region around m*n the fiducial point. • ‘1’ if higher, ‘0’ if lower. • Forms a binary pattern of ‘1’s and ‘0’s. • Converted to a decimal number
- 9. PLAN FOR BEB802 • Research, evaluate and implement feature selection algorithm, e.g. PCA (principal component analysis), mRMR or adaboost. • Research, evaluate and train classifier model, e.g. SVM (support vector machine), SVDA (support vector discriminant analysis). • Write android application. • Possibly train model with facial images from FEEDTUM or NVIE facial expression databases to improve accuracy.