age prediction using pca, lda and hybrid
DESCRIPTION
It is about PCA on Age prediction via Face.TRANSCRIPT
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Age Prediction using PCA, LDA and Hybrid MethodsMahdi Roozbahani and Shankar VishwanathCSE 6730 - Data and Visual analytics
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Agenda• 1. Objective
• 2. Applications and Use
• 3. Current Technologies
• 4. Methodology
• 5. Challenges
• 6. Results
• 7. Conclusion
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Objective• 1. Given an image, classify it to a certain age group.
• Using different methods (non-explicit)• 1. PCA• 2. LDA• 3. PCA + LDA Combination
• Cross Validate Results
• Study effects of leaving out ‘first n’ Eigenvectors, to negate shadow/light, camera effects,
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Where Used?• 1. Online Marketing and Advertisement
• 2. Security and Information Purposes
• 3. Age based Content Censorship.
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Current Technologies/Papers • 1. Belhumeur et al. seminal paper on ‘Recognition Using Class
Specific Linear Projection’.
• 2. They experimented with Fishers Linear Discriminant model and Eigenface to project image to low dimensional subspace.
• 3. Gao and Ai studied classification based on Fuzzy LDA method and Gabor features.
• 4. Still a lot to be explored.
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Current Technologies• Face.com has implemented age detection to its photo
scanning API.
• Crime department in Bristol experimented with Age Classification – result not too encouraging.
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Methodology: Idea
• Main idea: Use PCA and other approaches to find vectors that best account for variation of face images in image space.
• Images of faces being similar, will not be randomly distributed in space.
• Can be described by low dimensional subspace.
• We are looking at using PCA as a pre-processor and then use LDA.
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Methodology : Preprocessing
• 1. Data Acquisition and Preprocessing
• Obtain training data and sort them according to age groups
• Trim and resize the images to 61 X 49 pixels.
• Convert to grayscale.
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Methodology: FaceSpace• FaceSpace – PCA Approach
Normalize Data
• Center data by subtracting mean of all images
Build Covariance
Matrix
• Cov matrix is just multiplication of centered data with its transpose
Find Eigenvectors
• mutiply back by original matrix to get the eigen vectors.
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Methodology: FisherSpace• FisherSpace – PCA+LDA Approach
PCA Calc
Sb & Sw
Inv(Sb*Sw)Calc
Eigenvalues
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Methodology: Test Data• Multiply eigenvectors with the original training data to get
class training data.
• Project test data to training data and apply Classifier to determine age group.
• Use l2 Norm distance metric for classification• KNN classifier works too.
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Results
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Challenges• Accuracy highly dependent on data.
• Very difficult to obtain standard data with uniform lighting conditions, camera angles, facial expressions.
• Research papers used subjects with previous age tracked photos under controlled conditions.
• Addition of accessories, spectacles, hairstyles also affected results/accuracy.
• Perceived age not the same as actual age.
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Conclusions• Why PCA is Better than LDA at times?
• When would LDA+PCA work?
• How does leaving first few eigenvectors help?