1.
TensorFlow로
얼굴을 인식해 보자.
공개 소프트웨어 개발자 센터
(KOSS Lab. 2기)
Mario Cho (조만석)
hephaex@gmail.com

2.
Who am I ?
Development Experience
◆ Image Recognition using Neural Network
◆ Bio-Medical Data Processing
◆ Human Brain Mapping on High Performance
Computing
◆ Medical Image Reconstruction
(Computer Tomography)
◆ Enterprise System
◆ Open Source Software Developer
Open Source Software Developer
◆ Linux Kernel & LLVM
◆ OPNFV (NFV&SDN) & OpenStack
◆ Machine Learning (TensorFlow)
Book
◆ Unix V6 Kernel
Korea Open Source Software Lab.
Mario Cho
hephaex@gmail.com

3.
Contents
• 우리가 본다!
• 기초부터 MNIST
• CIF영상인식
• 학습 데이터 만들기
• Tensorflow에 적용
• 그리고 전설로.

4.
Human Intelligence

5.
Human Brain
Source: https://www.ll.mit.edu/publications/journal/pdf/vol 04_no2/4.2.5.neuralnetwork.pdf

6.
Neural network vs Learning
network
Neural Network Deep Learning Network
Human Neural network vs. Machine Learning network

7.
Traditional learning vs Deep Machine Learning
Eiffel Tower
Eiffel Tower
RAW data
RAW data
Deep
Learning
Network
Feature
Extraction
Vectored Classification
Traditional Learning
Deep Learning

8.
Caffe
U.C. Berkley 개발
C++, Python, MATLAB
대표적인 기계학습을 위한 공개 소프트웨어
* Source: http://caffe.berkeleyvision.org

9.
TensorFlow
* Source: https://www.tensorflow.org/

10.
Hierarchical Representation of Deep Learning
* Source: : Honglak Lee and colleagues (2011) as published in “Unsupervised Learning of Hierarchical Representations
with Convolutional Deep Belief Networks”.

11.
Hello World on TensorFlow

12.
Image recognition in Google Map
* Source: Oriol Vinyals – Research Scientistat Google Brain

13.
Hello World == MNIST
Data Sets data_sets.train 55000 images & labels
data_sets.validation 5000 images & labels
data_sets.test 10000 images & labels

14.
MNIST (predict number of image)

15.
MNIST beginner code

16.
Face recognition?

17.
CIFAR-10
• CIFAR-10
– 32x32 크기로 컬러 영상
– 10종류로 구분됨.
– TensorFlow 예제: tensorflow/models/image/cifar10/

18.
CIFAR-10 Model
Data Sets data_sets.train 50000 images & labels
data_sets.validation 1000 images & labels
data_sets.test 10000 images & labels
Source: http://www.cs.toronto.edu/~kriz/cifar.html

19.
CIFAR-10 data
• Image Data의 특징
– 10종류로 32x32로 된 인식 + 이미지로 구성
– 학습 데이터(Training data)
– data_batch_1.bin, data_batch_2.bin, … , data_batch_5.bin
– 테스트 데이터(Test data)
– test_batch.bin
• 1개의 이미지 파일(image file)
– 32(가로) x 32(세로) x 3(RGB) = 3072 byte
– 3 tensor image (RGB)
• *참조: 텐서 플로우의 이미지 형식
tf.image_summary(tag, tensor, max_images=3, collections = None, Name=None)
– [0, 255].unint8
– 1 tensor: Grayscale
– 3 tensor: RGB
– 4 tensor: RGBA

20.
CIFAR-10 learning network
Layer Name Description
conv1 convolution and rectified linear activation.
pool1 max pooling.
norm1 local response normalization.
conv2 convolution and rectified linear activation.
norm2 local response normalization.
pool2 max pooling.
local3 fully connected layer with rectified linear activation.
local4 fully connected layer with rectified linear activation.
softmax_linear Linear transformation to produce logits

21.
Source data: 얼굴 인식에 사용할 사진을 모으자!

22.
Prepare: 사진에서 얼굴만 추출하자.

23.
OpenCV

24.
Face Detection using Haar Cascades
Source: http://opencv-python-tutroals.r eadthedocs.io/en/latest/py_tutorials/py_objdetect/pya_face_detection/py_face_detection.html#face-detection

25.
Open CV: face dectec code

26.
CIFAR-10 data
• TFRecode file
– TensorFlow 에 TFRcords를 이용하여 binary 데이터를 연속된 데이터로 변형.
– 연속된 데이터를 Reader,Writer를 이용해서 읽고 쓸 수 있다.
– 고정 길이가 아닌 구조형 데이터도 읽고 쓰는 처리 가능
– 1 image file 은
– 32(가로) x 32(세로) x 3(RGB) = 3072 byte
– 1 label + 1 file image file 은
– < 1 x label > < 3072 x pixel>
– 1000 label + 1000 image file
– 32 * 32 * 3 * 1000 = 3 072 000 (3MB)
– Filenames, shuffling
– [“files0”, “file1”],
– (“file%d” % i) for I in range(2)
• tf.train.input_producer(input_tensor, element_shape=None,num_epochs=None,shuffle=True, seed=None,capacity=32,
shared_name=None,summary_name=None,name=None)
– input_tensor: 1차 열로 구성
– element_shape: (Optional.) 입력 텐서의 형태
– num_epochs: (Optional.)
– shuffle: (Optional.):데이터를 임으로 섞어 놓을지?
– seed: (Optional.):데이터의 씨앗을 사용?
– capacity: (Optional.): 버퍼를 사용한 입력.
– name: (Optional.) 큐의 이름

27.
CIFAR-10 data sets 형식으로 바꾸자.

28.
CIFAR-10 에서 대상을 얼굴 추출 자료로 바꾸자.

29.
TensorFlow Training
– 학습이 잘 된 것처럼 보이나되어서 평가를 해보면

30.
CNN layer를 바꿔가며 자신만의 방법을…
• X2 conv layers
96 x 96 x 3
48 x 48 x 32
24 x 24 x 64
12 x 12 x 128
6 x 6 x 256
(6 x 6 x 256) * 1024
1024 x 256
256 x 5
Cf. weight compare
- cifar10:
- (5 x 5 x 3 x 64) + (5 x 5 x 64 x 64) + (4096 * 384) + (384 * 192) + (192 * 6)
= 1754944
- X2 conv:
- (3 x 3 x 3 x 32) + (3 x 3x 32 x 64) + (3 x 3 x 64 x 128) + (3 x 3 x 128 x 256) + (9216 x 1024) + (1024 x 256) + (256 x 5)
= 10088544 (5.7x cifar10 )

31.
CNN 2x code

32.
소결!
• CIFAR-10을 응용하여 얼굴 인식을 해보자.
• 이쁜 언니들 얼굴 자료를 모으고
• 전체 사진에서 Open CV를 이용해서 CIFAR-10에
맞게 얼굴만을 데이터를 수정해서
• CIFAR-10 형식으로 데이터 변환했다.
• 그리고 python cifar-10을 실행하니
• 학습까지 잘되었다? 진짜!?!

33.
뭐
가
문
제
냐
?

34.
face extraction

35.
#1. No. of Face data

36.
# 2. Face extraction method

37.
#3. advanced face detect

38.
#4 View side effect
Source: http://socialsciences.uow.edu.au/psychology/research/pcl/members/UOW040587.html

39.
#5 Emphasis?

40.
#6. Eigen faces
Source: http://jbhuang0604.blogspot.kr/2013/04/miss-korea-2013-contestants-face.html

41.
#6. Converging on the same face
Source: http://jbhuang0604.blogspot.kr/2013/04/miss-korea-2013-contestants-face.html

42.
Make Her Beautiful?
Source: http://www.estherhonig.com

43.
Thanks you!
Q&A