Face detection uses computer vision and image processing techniques to classify and localize faces within images. It involves detecting faces, identifying key facial features, and determining their locations. Common methods include semantic and instance segmentation using convolutional neural networks, as well as YOLO-based approaches that divide images into grids and predict detection bounding boxes and confidence scores for each grid cell. Face detection performance is typically evaluated using metrics like average precision (AP) and mean average precision (mAP) which measure accuracy of localization across different probability thresholds or object sizes. It has various applications including face unlock, person identification, and video surveillance.

1. Introduction to Face Detection
By: Amar Jindal
NIT Patna

2. Face Detaction or Object Detection
 Combination of computer vision and image processing to find out object in
a image or video.
 The Object detection includes classifying an object in the image and
localize its poition in the image.
 This can be achived using image segmentation with AI.

3.  Classify the face.
 Localize the face
 List of facial
features.
How it Looks like?

4. Semantic
Segmentation
Instance
Segmentation
Types of Segmentation

5. Computer Vision Tasks
Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf

6. Classification using LeNet on MNIST dataset

7. Computer Vision Tasks
Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf

8. Classification + Localization
● Classification:
○ Input: Image
○ Output: Class label
○ Loss: Cross entropy (Softmaxlog)
○ Evaluation metric: Accuracy
● Localization:
○ Input: Image
○ Output: Box in the image (x, y, w, h)
○ Loss: L2 Loss (Euclidean distance)
○ Evaluation metric: Intersection over Union
● Classification + Localization:
○ Input: Image
○ Output: Class label + box in the image
○ Loss: Sum of both losses

10. Convolution
Operation

11. Poolin
g

12. Cross Entropy
 Measures the performance of a
 classification model whose output
 is a probability value between 0 &
1.
 Increases as the predicted P(e)
 diverges from the actual label.

13. Euclidean Distance
 This loss function is usually used for regression problems,It measures the
 distance between two distinct points.

14. Shallow
Features
Deep
Features
Feature
1
Feature
2

15. Classification + Localization: Model
Classification
Head:
● C Scores for C
classes
Localization Head:
● Class agnostic:
(x,y,w,h)
● Class specific:
(x,y,w,h) X C

16. Computer Vision Tasks
Source: CS231n Object detection http://cs231n.stanford.edu/slides/2016/winter1516_lecture8.pdf

17. Intersection Over Union (IoU)
● Important measurement for object localization.
● Used in both training and evaluation.

18. IoU
Algorithm

19. Face Recognition using Siamese Networks
 This network allows to calculate the degree of similarity between two
inputs

20.  Since we can not have multiple images of the same person in
our database,

21. Yolo Based Face Detection
Each grid cell has 5 associated values. The first one is the probability p of that cell
containing the center of a face.The other 4 values are the (x_center, y_center, width,
height) of the detected face (relative to the cell).

22.  2x [8 filter convolutional layer on 288x288 image]
 Max pooling (288x288 to 144x144 feature map)
 2x [16 filter convolutional layer on 144x144 feature map]
 Max pooling (144x144 to 72x72 feature map)
 2x [32 filter convolutional layer on 72x72 feature map]
 Max pooling (72x72 to 36x36 feature map)
 2x [64 filter convolutional layer on 36x36 feature map]
 Max pooling (36x36 to 18x18 feature map)
 2x [128 filter convolutional layer on 18x18 feature map]
 Max pooling (18x18 to 9x9 feature map)
 4x [192 filter convolutional layer on 9x9 feature map]
 5 filter convolutional layer on 9x9 feature map for the final
grid

23. Auxiliary network
Outputs of the main network were not as accurate as expected. Hence, a small
CNN network was implemented to take as input a small image containing a face

24. Object Detection
l
● Input: Image
● Output: For each object class c and each
image i,an algorithm returns predicted
detections: ocations
with confidence scores .

25. Object Detection: Evaluation
● Today new metrics are emerging
○ Averaging precision over all IoU thresholds: 0.5:0.05:0.95
○ Averaging precision for different object sizes (small, medium, big)
○ Averaging recall as a metric to measure object proposal quality.

26. Average Precision (AP)
● [In the vision community] AP is the estimated area under the PR curve

27. Mean Average Precision (mAP)
● The winner of each object class is the team with the highest average precision
● The winner of the challenge is the team with the highest mean Average
Precision (mAP) across all classes.

28. Object Detection: Evaluation
● Mean Average Precision (mAP) across all classes, based on Average Precision
(AP) per class, based on Precision and Recall.

29. Applications
 Face Unlock
 Person Identification
 Video Survillance

30. Doubts..?