Effect of Hough Forests Parameters on Face Detection Performance: An Empirical Analysis M. Hassaballah, Mourad Ahmed and H.A. Alshazly Department of Mathematics, Faculty of Science, South Valley University, Qena, 83523, Egypt. The 9 th IEEE International Conference on Computer Engineering and Systems (ICCES) Dec , Cairo, Egypt

Statement of the Problem Motivations and Objectives Random Decision Forests Approach Proposed Training Algorithm Proposed Detection Algorithm Random Forest Model Parameters Comparisons with other methods 2 Overview

Statement of the Problem The challenging machine-vision task that has been addressed by the research in this paper is the automatic detection of human face Fig. 1: Detection of human face in the input image gray/color images. Gray Scale Image Color Image 3

Motivations and Objectives There are many potential applications for automatic face detection, some of these applications are as follows:  Face Recognition/Verification. Recognition by parts approach to deal with occluded images to overcome the restrictions of holistic face recognition approaches.  Facial Expression Analysis. Expressing a person's desires, needs, cognitive processes, and emotional state.  Audio-Visual Speech Recognition. Through analyzing the subtle cue conveyed by lip movement of speakers (i.e., lip-reading).  Face Animation and 3D Face Reconstruction. where the goal is to create realistic virtual, emotive faces for both movie and game industries depend basically on the fiducial points.  Many other applications in HCI field. Fig. 2 : Applications of the face detection. 4

Random Forests Tree Model Binary tree tests Binary tree graphical structure 5 Right Left 0 = true, Go Left 1 = false, Go Right

Building the Trees (Mathematical Notation) Image Patches set Appearance of the patch extracted features by different channels Class Label, background, face Relative position to Face center (for Face training set only) Patches locations generated randomly using the uniform distribution. 6

Face Facial Patches (positive) 7 Non-face training set samples Face training set samples Training set

8 Initialize forest with number of trees Initialize forest with number of trees Initialize Patches Width and Height Initialize Patches Width and Height Extract Patches Save forest Train forest Training flow charts

Binary Tests Selection Test with optimal split where Class-label uncertainty – With Shannon entropy Offset uncertainty Where the Type of uncertainty measurement is randomly selected for each node 9

a)Number of patches = 10 b)Number of patches = 20 c)Number of patches = 30 d)Number of patches = 50 e)Number of patches = We use 50 patches per image in face training set Some missed facial features are not covered All facial features are covered Number of patches per image

(b) 16 Features channels of Min filter Patch facial appearance The extracted features of the patches are: 3 channels CIELAB color space, to avoid the effect of illumination 4 channels of the first and second Sobel derivatives (x,y directions) 9 channels of HOG-like descriptor. To increase the invariance under noise, we apply min and max filtration (a) 16 Features channels of Max filter 11 Extract features channels

Leaves 12

Detecting flow charts 13 Draw Bounding face box Hough forest trees traversal Extract patches Hough image Searched for maxima Input image Patches Voting

Hough Transform & Probability For location y and given image patch and tree T Over all trees Accumulation over all image patches (into Hough Image) 14

Detection with Hough Forests (a) Extract patches(b) Vote for all leaves(d) Detecting faces(c) Hough image 15

Multi-Scale - The test image is resized by a set of scale factors - The Hough images - Hypotheses Face bounding box centered at the point has the size - Multi Scale 3D Vectors (x, y, scale) 16

Forest Parameters The maximum allowed tree depth. Forest size. Patches size. The amount of randomness and its type; the choice of features in practical applications. 17

(b) Effect of Forest Size F (a) Effect of Tree Depth Dmax 18 Forest Parameters

(d) The Effect of Randomness (c) The Effect of Patch size 19

Forest Parameters Details of generating forest trees using various depth values. 20

Comparisons with other methods Our method CMU+MIT database 21

Comparison detection examples using Viola & Jones’ method (green) and Hough forests method (red) on test images from the CMU+MIT database. 22