A survey on different Facial recognition algorithms as well implemented on various standard dataset. Tested different feature extraction and dimensionality reduction methods to arrive at a better accuracy.

1. Face Detection Using Computer Vision
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Nimai Chand Das Adhikari

2. • Plays an important role in many applications such as video
surveillance and face image database management
- Each person has a particular face structure
- Facial symmetry
• A survey on:
- Different Facial recognition algorithms as well its implementation
- Tests of different feature extraction and dimensionality reduction
methods
A Brief Note

3. Steps FollowedSteps Followed

4. Layers:
1. Convolutional Layers:
To extract different features
of the input image with
RELU activations
2. Pooling Layers:
Max operation on the
obtained feature map vector
3. Fully Connected Layers:
To predict the probabilistic
labels
CNN: Convolutional Neural Network

5. Architecture Used
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Architecture Description

6. Face DetectionFace Detection Process
Use of Open CV
Steps:
1. Open CV: Computer Vision
Package
2. Detect the Face and Eyes

7. Results Achieved
• Dataset: Yale Face
Database
• Size: 6.4MB
• # of Images: 165
• 15 individuals
• 11 images/subject
Network Output Graph
Results Output
Dataset Used

8. p. 8
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