Summary of a personal project to train a simple model and recognize my son's face.

1. How to Recognize
Henry’s Face
Henry’s Father - Yongqiang Li

2. MNIST Sample in Tensorflow Tutorial
Train Data: 60000, 28x28
Test Data: 10000, 28x28

3. My Goal
Henry - 1
Not Henry - 0

4. How My Data Comes From
 Henry’s Faces – I took a lot of pictures of Henry

5.  How about others’ faces? - Network Crawler

6.  Use Python + Selenium to implement the crawler
 Got about 20K images.
 My keywords are
keywords = ['happy face', 'smiling', 'face', 'crying', 'beauty', 'fear face', 'worker', 'engineers', 'kid', 'boy', 'girl', 'man', 'model', 'women']

7. Prepare the Data
 How to get the face from the image?
 OpenCV
 HAAR Classifier

10.  Finally! I got many faces.
 Labeling: The file name ends with “_1” is Henry’s face. With “_0” is others.

11. 630x684=430920 pixels with RGB color 630x684=430920 pixels with grayscale 30x30=900

12. Model Train (1)
遍历图片文件夹
读出Label信息
转换图片为矩阵
Shuffle数据顺序
Zip 矩阵和Label信息
切分Train数据和测试数据

13. Model Train (2)
把[0,255]灰度变为[0,1]浮点
构建一个Neutral Network
设置训练参数
开始训练
测试训练结果
保存训练的模型

14. Model Train (3)
 My NN has only 3 Layers.

15. Model Train (4)
 GoogLeNet Inception v4 has 22 layers. – Data Scientist

16. Model Train (5)

17. Model Train (6)
Accuracy is 95.5%
Anyway, my goal is very simple. And 3 layers seem good enough.

18. Model Predication

19. Results

20. But when smiling…
More work to do!

21. References
 Face Detection using HAAR Cascades
 Face Recognition with Python, in Under 25 Lines of Code
 Tensorflow Tutorial – Basic Classification
 Going Deeper With Convolutions