Modified Haar-Cascade Model for Face Detection Issues

Modified Haar-Cascade Model for Face Detection Issues

A. Singh, H, Herunde, F. Furtado

Department of Master of Computer Application, Jain University, Knowledge Campus, Bengalore,

Karnataka, India.

A B S T R A C T

Amid the previous three decades, the topic of image processing has gained vital name and recognition

among researchers because of their frequent look in varied and widespread applications within the

field of various branches of science and engineering. As an example, image processing is helpful to

issues in signature recognition, digital video processing, remote sensing and finance. Image

processing models are used for detecting the face. The aim of this thesis is to solve the face-detection

in the first attempt using the Haar-cascade classifier from images containing simple and complex

backgrounds. It is one of the preeminent detectors in terms of reliability and speed. We introduced a

new method to deal with the frontal face images by using a modified Haar cascade algorithm. By

using this algorithm, we can detect the image as well as the coordinates. The main attraction of this

paper is to solve different types of images having one object, two objects, and three objects which

can’t be solved by any of the existing methods but can be solved by our proposed method.

Keywords: Face detection, Haar cascade classifier, OpenCV, NumPy, Python, Machine learning.

Article history: Received: 13 November 2019 Revised: 28 March 2020 Accepted: 05 May 2020

1. Introduction

Information technology has become an integral part of our life. To satisfy the need of society,

almost in each work, we use technology. In the current era, computer science is a major subject.

It has many real-life applications such as the internet of things [1–8], SPP [9–16], transportation

problem [17, 18], Powershell [19], uncertainty [20–23] and so on. Information technology is the

mode by which users can use computers and the internet to store, fetch, communicate, and utilize

the information. So all the organizations, industries and also every individual are using computer

systems to preserve and share the information. Image processing plays a major role in all

computer-related applications e.g. education sector, home security, defense system, banking

Corresponding author

E-mail address: [email protected]

DOI: 10.22105/riej.2020.226857.1129

International Journal of Research in Industrial

Engineering

www.riejournal.com

Int. J. Res. Ind. Eng. Vol. 9, No. 2 (2020) 143–171

Singh et al. / Int. J. Res. Ind. Eng 9(2) (2020) 143-171 144

system, and so on. This manuscript aims to classify the images containing simple and complex

background detecting the face in the first attempt using Haar-cascade.

Face detection is a type of identification. When we see any person face, then we will get

information like gender and age, etc. Face detection is used in applications such as human-

machine interaction, gender classification, surveillance system, bio-metrics, etc., it is very

difficult to detect the face.

2. Face Detection Methods

Face detection has always been a part of face identification. There exist four different face

detection methods which are shown at Figure 1.

Figure 1. Different techniques for face detection.

2.1. Knowledge-Based Method

This method depends on the agreement of rules. The main problem of these strategies is to build

a reasonable rule. This method is incomplete and weak to search for many countenances in

multiple images.

2.2. Feature-Based Method

This method finds image displacements that are easy to understand. The feature-based method

prepares as a classifier and after that, it is used to separate the non-facial and facial sections.

2.3. Template Matching Method

This method uses the parameterize face to detect the faces by the connection between input and

template image. Additionally, by using the edge detection method we can detect face models

easily.

145 Modified Haar-cascade model for face detection issues

2.4. Appearance-Based Method

This method depends on a set of training face image to detect the face. And it considers a high

dimensional vector. Appearance-based method uses some techniques like statistical analysis and

machine learning.

Some appearance-based model which is shown below:

Eigenface-based algorithm.

Support vector machine algorithm.

Neural-networks technique.

2.4.1. Eigenface-based algorithm

This method mainly was used for face recognition, Eigenface based method using the principle

component analysis.

2.4.2. Support vector machine algorithm

This algorithm is a linear classifier. This classifier maximizes the boundary between the

resolution hyperplane and the set of the training image.

2.4.3. Neural-networks technique

Using neural networks technique we can detect methods like facial detection, face detection,

object detection, etc.

3. Literature Review

Roy et al. [24] proposed a model for solving non-frontal face with variation in their alignment.

For this purpose, they consider video streaming images Singh et al. [25]. The major problem of

face detection while using a Haar cascade classifier is that the image contains both simple and

complex background. Additionally, the Indian Face Database (IFD) and Caltech library are

considered as a standard database. Roy and Podder [26] discussed some important properties of

face detection which is used to solve real-time problems such as facial expression recognition,

face tracking, facial feature extraction, gender classification, identification system, document

control, access control, clustering, bio-metric science, and Human-Computer Interaction (HCI)

system. Parkhi et al. [27] proposed a model to recognize a face from either a single photograph

or from a set of faces tracked in a video. Their model works on two principle i.e. a) end to end

learning for the task using a Conventional Neural Network (CNN) and (b) the availability of very

large scale training data sets. Kadam and Ganakwar [28] did an extensive and critical survey of

existing literature on human face detection systems. Sun et al. [29] proposed a new model for

detecting faces using deep learning. They improved the Retina Convolution Neural Network


(RCNN) framework by adding several approaches, including features such as hard negative

mining, concatenation, model retraining, multi-scale training, and proper calibration of key

parameters. Puri et al. [30] developed a system that can analyze the image and predict the

expression of the person. For this purpose, they used NumPy, Python, and (OpenCV) tools. Some

significant influences of image processing have been presented in Table 1.

3.1. Different Researcher’s Contributions

Some of the major contributions in image processing are discussed in Table 1.

Table 1. A literature review of image processing.

Table 2 discusses the contribution of the Haar cascade method used for a different purpose.

Table 2. A literature review of Haar cascade technique.

One undertaking from the above discussions is that image processing has gained vital name and

recognition among researchers because of their frequent look in varied and widespread

applications within the field of various branches of science and engineering. Additionally, the

Authors Years Different Approaches to Solve Image Processing

Klug & Rosier [31] 1966 The authors proposed a method for digital image processing of two-

dimensional images.

Billingsley [32] 1970 The author proposed a few applications on digital image processing.

Andrews and Patterson

[33] 1976

Authors have proposed a singular value decomposition technique for digital

image processing.

Goetcherian [34] 1980 Converted from binary to grey tone image processing using uncertainty theory.

Burt [35] 1981 The author used a fast filter transforms for image processing.

Sternberg [36] 1983 The author proposed a biomedical image processing system.

Umbaugh [37] 1997 The author proposed a new approach for solving computer image processing

using Cviptools with Cdrom. Lehmann et al. [38] 1999 They did an extensive survey of medical image processing.

Plaza et al. [39] 2009 Proposed a new technique for solving hyperspectral image processing.

Authors Years Different Approaches to Solve Image Processing

Viola and Jones [40] 2001 The author introduced the Haar cascade method for the first

time.

Lienhart and Maydt [41] 2002 They implemented the idea of a tilted (45°) Haar-like feature.

Messom and Barczak [42] 2006 They extended the idea of a generic rotated Haar-like feature

technique.

Haselhoff and Kummert [43] 2009 They proposed an algorithm for vehicle detecting using Haar

and triangular features.

Angriani et al. [44] 2014 Proposed a model based on viola jones approach for detection

many faces.

AbdelRaouf et al. [45] 2016 They proposed a recognition model for Arabic character

detection using a Haar cascade classifier.

Daliman et al. [46] 2016 They proposed a recognition model for young oil palm tree

using Haar-based approach.

Meduri and Telles [47] 2018 They proposed a smart parking system using Haar cascade.


higher literature review reveals that there are gaps in the study of image processing. As such, the

subsequent gaps are studied:

This technique is unable to moderate false detection rate.

Even the non-face skin color region can also be detected sometime.

Some of the existing methods have a high false rate.

Therefore, this motivates us to provide a new model for society.

Haar-cascade classifier is a documented technique for face-detection. The roles of this paper as

follows:

This approach helps to resolve a new set of problems.

We introduced a new method to deal with the frontal face images by using a modified Haar

Cascade algorithm.

The proposed method has rapid image processing with high detection rates.

Provides a very less false positive rate.

The main attraction of the paper is to solve different types of images having one facial object and

two facial objects on an image.

4. Discussion of Existing Method

4.1. Face Detection Based on Skin Color

This method is very much useful and one of the most popular and useful face detections. Human

faces have different colors by using skin color method have several advantages [48].

Facial features are less fast than color processing. In this method, each pixel was divided into the

skin or non-skin based on color which is shown in Figure 2.

In this term where the color statement plays the main role, the HSA model stands number one

than the RGB model. Now, this detection is to separate the colored image into the skin and non-

skin section. Each color image has various ranges of pixels [49-51].

Figure 2. Face detection output [28].


Once the segmentation procedure is done then with the structuring element, the morphological

operators are implemented. After applying this morphological operator [52], the standard

variation of the area is calculated and the rectangle is shown in the skin section. It is removed

when unnecessary rectangles are created.

4.2. Viola-Jones Face Detection system

It is an object detection frame-work [40]. It is providing robust object detection and competitive

object detection in the real world. This method was purposed by Viola and Jones [40]. Even

though it is trained to detect a glut of object classes, it was stimulated by face detection. We can

detect the image rapidly with high detection using the face detection framework. The three

principles of the face detection framework have proposed below.

4.2.1. Integral image

It is a new image description [40]. It allows detecting the characteristics more quickly. At once

this integral image is done any scale or the location in the even time the Haar cascade feature can

be implemented. The sum of the pixels above and to the left of (x,y) is the integral image at the

location (x,y) [48]. The integral image processing technique is shown in Figure 3.

4.2.2. Adaboost algorithm

Adaboost algorithm is prepared by selecting a small number of rectangle features. From a huge

number of features computed in stage 1, we focus on only choosing few features that will give

us to detect face with great accuracy.

Figure 3. Conversion of the image into an integral image [28].

For this, we are using Adaboost algorithm [53] to choose the main features and to prepare

classifiers that we can use them. This algorithm is used to create a strong classifier from a linear

grouping of the weak classifiers. AdaBoost algorithm provides an emphatic learning algorithm.

From selecting a small number of rectangle features, the Adaboost algorithm is prepared. Stage

1 has a huge number of features in that we need to choose only very few features that give great


accuracy when we detect the face. So for this purpose, we are using the Adaboost algorithm [53]

to have main features and to have a classifier that we can use.

From the linear grouping of the weak classifiers, we can create a strong classifier using this

algorithm. The algorithm provides an emphatic learning algorithm. It is also been noticed that

the existing models have some disadvantages which are discussed below:

This technique is unable to moderate false detection rate.

Even the non-face skin color region can also be detected sometime.

There are so many images in the real world that have skin tone colors, for example: Skin, leather,

wood, sand, etc. which can be detected mistakenly.

5. Description of the Research Work

5.1. Research Problem

OpenCV provides a Haar cascade classifier. We have used the default frontal face in our method.

There are some databases used for Experiment. It is shown in Table 3.

Table 3. Consider the data set.

5.2. Solution Methodology

There are so many methods to detect the faces; face detection can be done with obtaining higher

accuracy by using this method. This method has a similar process like face detection in the neural

networks, open CV, MATLAB, etc. Here we will be working on OpenCV for detection of the

face and these are some of the steps.

Table 4. Algorithm for the proposed method (see Figure 4).

Databases Total Image

The test set 1: Single frontal face images 25

The test set 2: Multi frontal face images 25

Databases Total Image

The test set 1: Single frontal face images 25

The test set 2: Multi frontal face images 25


Flow chart of the proposed algorithm is as follows.

Figure 4. Flow chart of the proposed algorithm.

Firstly we will set the path of the image. Then the image is converted from RGB to grayscale

because it is easy to detect faces, which is shown in Figure 5.

Figure 5. Converting RGB image to grayscale [56].

After that, if it is needed we can resize, crop blur and sharpen the image. Image segmentation is

the next step which is used for shape and multiple objects in one single image so that the object

and faces can be detected using this method easily.

So now, we are using Haar-like features algorithm, developed by voila and Jones [40] for

detecting the face using Haar-like features; we can find the exact location of the face present in

the image. All the faces have qualities like the eyes, nose, and eye, etc. With the help of line

detection, edge detection, center detection (see Figure 6) which are used in the algorithm, help us

for feature selection or feature extraction for an object in the image such as eyes, mouth, etc.


Figure 6. Haar-like features for face detection [56].

The next step is to provide the coordinates of x, y, w, h which create a rectangle box in the image

to show the position of the face. After this, it will make a rectangle box where it will detect the

face which is shown in Figure 7.

Figure 7. Haar-like features for face detection [56].

5.2.1. Description of proposed method

We create a cascade classifier object to pull out the facial appearance of the face. The XML path

contains the face Feature. Now we would be to read an image of the face and switch it into a

black and white image using COLOR BGR2GREY. After that, we search for the coordinates for

the face. It will be done using detect MultiScale. The scale factor decreases the image shape by

5% until the face is detected. Now we are adding the rectangle face box. Check out the following

image. Which is shown in Figures (8)-(9).


Figure 8. Successfully detect the face in an image.

Figure 9. Workflow of the proposed model.

6. Result and Discussions

In this section, we show the graphs under different environments where our proposed methods

able to identify the human face object. The maximum and the minimum brightness stages and

the RGB graph show the accuracy of the proposed method.

Now consider one picture from the set 1 and execute our proposed algorithm and we get the

following results which are shown in Figures (10)-(28).


Figure 10. Consider one picture from our data set 1 [54].

Figure 11. Considering the single face object.

Figure 13. After increasing the brightness of Figure 10.


Figure 12. Histogram of the RGB graph for the image (see Figure 11).






Figure 17. Again slightly increasing the brightness of Figure 15.



Figure 19. Again slightly increasing the brightness of Figure 17.


Now consider the single frontal face image (see Figure 10); we aim to reduce the brightness and

observation is shown in Figures (20)-(28).


Figure 21. Slightly reduce the brightness in Figure 10.











Now consider one picture from the set 2 and execute our proposed algorithm. We get the

following results which are shown in Figures (29)-(43).


Figure 29. Consider one picture from the data set 2 [55].

Figure 30. Considering the double face object.











Now consider the same picture (see Figure 28) from the set 2 and execute our proposed algorithm

after reducing the brightness. We get the following results which are shown Figures (38)-(43).


Figure 38. After decreasing the brightness of Figure 28.

Figure 39. Histogram of RGB graph for the image (see Figure 38).







7. Conclusion

In this article, we solve the face detection using Haar cascade classifier in the very first attempt

from the image which has a simple and complex background. In terms of reliability and speed, it

is one of the preeminent detectors. We introduced a new modified method to deal with the frontal


face images by using a modified Haar cascade algorithm. By using this algorithm, we can detect

the image as well as the coordinates. The main attraction of the paper is to solve different types

of images having one object and two objects which can’t be solved by any of the existing methods

but can be solved by our proposed method.

8. Acknowledgments

I would like to express my sincere feeling and obligation to Dr MN Nachappa and Prof. Subarna

Panda and project coordinators for their effective steerage and constant inspirations throughout

my analysis work. Their timely direction, complete co-operation and minute observation have

created my work fruitful.

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Modified Haar-Cascade Model for Face Detection Issues