Abstract

In current report, method for face detection and recognition based on visual data is proposed. For face detection Viola-Jones algorithm with Haar like features estimation based on cascade architecture were studied. The local binary patterns descriptor used for face recognition stage. Face recognition technique based on visual processing is significant for many applications. For example in the personal information protection, human-machine interaction, proctoring providing in distance learning platforms, access on the territory of objects with high security level e.t.c. Facial recognition approaches also vary considerably. At the initial stage of development of approaches to face recognition, geometric features were used to highlight the characteristic facial features [1, 2]. Nowadays, to solve this problem, deep learning technologies [3, 4], evolution algorithms [5], particle swarm method [6] and other approaches. On face recognition efficiency may influence various factors like varying expressions and illumination poor [7, 8], subject's pose variations [9], the own-age, gender, and -ethnicity [10-12] etc.

Keywords

• face recognition
• face detection
• local binary pattern
• Viola-Jones algorithm
• Haar-like features

Introduction

2.1

Face detection

In first face identification algorithm step one-against-all classification was used. This classification divides image objects into two classes “face” and “no-face objects”. The Viola-Jones algorithm is identified as one of the classic approaches to solving the problem of face recognition [ 13 ]. The main area of application of the Viola-Jones method is the problem of face detection [ 14, 15 ]. The basis of the work of the Viola-Jones method is the identification of the Haar like features and the use of a cascade classification model. A feature of the Viola-Jones method is to work with an integral way of representing the image. The integral image representation is a matrix with the same size as the original image. Each of its elements contains the sum of the pixel intensities locating to the left and above the current element. integral image representation elements for each original image pixel are calculated by the Eq. 1. (1) where I(i, j) is the original image pixel intensity, (i, j) its coordinates. Thus, each element of the matrix L is the sum of the intensity values of pixels in a rectangle from a pixel (0, 0) to a pixel with coordinates (x, y).

The scanning window consisting of adjacent rectangles - the Haar primitives - is moved across the video image being examined to calculate the Haar-like features. It becomes possible to separate face from other image objects by selection such characteristic of the pixels intensity change features. By moving the scanning window over the entire image, the Haar-like features are calculated. These features show the intensity difference value on the region of interest. In the present work basic and additional Haar masks were used (Fig. 1). Use of additional Haar masks allows detecting faces with different angles of rotation to the camera, even faces facing the camera with a rotation angle of 2.1

Face recognition

The second step is face recognition using local binary pattern (LBP) texture features estimation. The LBP descriptors are computing for person identification. Classification based on local binary pattern operator is widely uses in many applications [ 17-19 ]. To personal identification a face image is broken into non-intersecting blocks. The LBP was introduced by Ojala et al. [ 20 ] as a binary operator robust to lighting variations with low computational cost and ability of simple coding of neighboring pixels around the central pixel as a binary string or decimal value. The use of local binary patterns for solving the face recognition task is shown in Fig. 2. more than 30 degrees [ 16 ]. At the next stage the Haar-like features in the Viola-Jones algorithm are organized into a cascade classifier. The result of the Viola-Jones classification algorithm is a set of attributes for each area, consisting of 200 values of differences in intensity, allowing separating images containing a face from images without it.

The operator LBPR(P) is calculated in a surrounding relatively a central pixel with intensity Ic by Eq. 2, where P is a number of pixels in the neighborhood, R is a radius, Ic and In Y component values is from YUV color space. If (In – Ic)  0, then s(In – Ic) = 1, otherwise s(In – Ic) = 0. Binary LBP code is computing as follows: the current LBP bit is assigned the value “1” if for the current pixel the Y intensity is less than the central pixel intensity, “0” otherwise. In 225 33 … 169 this manner is calculated P-bit binary LBP code that describes a pixel neighborhood. In this paper, we take into account 8 intensity values of neighboring pixels, that is, the radius R=1, to construct LBP binary code. The pixels are traversed clockwise, the bit width of the LBP binary code is 8.

Then a binary code is transformed to decimal code. For histogram computing amount of equal numbers is calculated defining a position and a height of histogram columns. The constructed histograms for different parts of the face are concatenated into one histogram. Chi-square distance, histogram intersection distance, Kullback-Leibler divergence, and G-statistic are usually used during classification stage. In this research, the Euclidian Distance by Eq. 3 was chosen for histogram comparison as the most recommended metric.

P1 LBPR (P)   s  In  Ic   2n

n0 D  n (hist1i  hist2i )2 , i1 (2) (3) where hist1i – column with number i of studied face image histogram, hist2i – column with number i of the face image histogram from available facial dataset, n - the number of histogram columns. The block diagram of the algorithm for using the local binary pattern operator for the face recognition task is shown in Fig. 3.

Start

Image selection

Image is divided into blocks

Block selection

Reading the pixel intensity value, Ic

Ic < Ii yes s(x)=1 no

no s(x)=0 no

Adding s (x) into LBP code Is the end of the block reached? yes

Converting binary LBP code to a decimal number Is the whole image passed?

yes no

No match found

Histograms concatenation

The calculation of the Euclidean distance

Threshold value greater?

User identification done yes End

Histogram computing

Thus, the combined histogram of facial fragments is compared on a threshold with each of the reference histograms, based on this comparison, user identification is performed. 2

Experimental and results

Experimental studies for the stages of face detection and recognition by video data were carried out separately. For face detection stage sample videos, including 4916 examples of individuals and 8,500 examples with no faces taken from the dataset Labeled Faces in the Wild Home [ 21 ] and the dataset Aberdeen [ 22 ] were applied. To verify the quality of the face recognition algorithm, the YouTubeFaces (YTF) [ 23 ], McGillFaces Database [ 24 ] and Db Fases Dataset [ 25 ] datasets were used. The dataset contains YouTubeFaces 3425 of video images 1595 different people, recruitment McGillFaces Database 60 videos with images of 40 different people set Db Fases Dataset 22 of the video with images of 38 different people. Video images have different levels of illumination, contain a different number of people of both sexes. At the same time, the number of people and the angle of rotation of the head of person to the camera differ in the videos. Images have different sizes from 160 × 120 pixels to 1280 × 720 pixels. The images contain both natural and human-made objects, people. In this case, video shooting was performed both indoors and outdoors. In addition, people were completely free in their movements, which led to arbitrary scale and facial expressions, the position of the head. The videos were in mp4 or avi format. Examples of frames of used videos are shown in Table 1.

YouTubeFaces\P1E_S2_М6.mp4.

Number of frames:1025

Resolution: 640×480.

Number of faces: 3 female faces.

YouTubeFaces\P1E_S2_М1.mp4.

Number of frames:875.

Resolution: 640×480.

Number of faces: 1 female face, 1 male face.

YouTubeFaces\P1E_S2_М3.mp4.

Number of frames: 750.

Resolution: 1280×720.

Number of faces: 2 male faces.

McGillFaces Database\mmdm2\ video \sx372.avi.

Number of frames: 400

Resolution: 640×480.

Number of faces: 1 female face и 1 male face.

YouTubeFaces\P1E_S2_D5.mp4.

Number of frames: 900.

Resolution: 1280×720.

Number of faces: 2 male faces.

YouTubeFaces\P1E_S1_С3.mp4.

Number of frames: 1250.

Resolution: 1280×720.

Number of faces: 1 male face. Number of faces: 2 male faces. McGillFaces Database\mmdm2\ video\sx102.avi.

FAR, % 0,00 0,00 0,00 0,00 0,00 0,00 5,80 0,00 0,00 4,70 0,00 TR, % 99,5 100 99,1 97,5 96,2 100 87,9 100 100 88,9 98,2

Face recognition

FRR, % 0,50 0,00 0,01 2,50 4,00 0,00 12,1 0,00 0,00 11,1 1,80

As shown by the results of experimental studies, gender and age of people do not affect the quality of the algorithm for detecting and recognizing faces. The quality of the algorithm is influenced by such factors as scene illumination level, video resolution, speed of people moving on the stage, face rotation angle and the face openness degree. Thus, an additional error in the algorithm of face detection and recognition is made by accessories worn on the face, such as glasses, scarves, hats. Negative impact is also closing part of the face with hair, beard or mustache. Emotional facial expression in most cases does not affect the results of the algorithm, but it can cause difficulties in recognition, for example, with a wide smile or closed eyes of a person. In addition, when shading a part of the face, the quality of the algorithm may decrease.

Thus, the solution of the problem of facial recognition today is relevant for the implementation of various practical tasks. In the present work, the Viola-Jones algorithm was used to face detection stage; local binary patterns were used for facial recognition. Experimental studies conducted on heterogeneous video data confirm the effectiveness of the proposed methods.

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