The document discusses face detection using computer vision, highlighting its significance in applications like video surveillance and database management. It surveys various facial recognition algorithms, feature extraction methods, and details the convolutional neural network (CNN) architecture used, which includes convolutional, pooling, and fully connected layers. Results are based on a dataset from the Yale face database, comprising 165 images of 15 individuals.

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
+ =
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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