Face Recognition Using Cascaded Object Detector in MATLAB

DOI : 10.17577/IJERTV7IS090036

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Face Recognition Using Cascaded Object Detector in MATLAB

Face Recognition Using Cascaded Object Detector in MATLAB

Vaishali 1,

1 M. Tech.., Research Scholar, Department of Computer Science and Engineering,

PDM University, Bahadurgarh

Banita 2

2Assistant Professor,

Department of Computer Science and Engineering, PDM University, Bahadurgarh

Abstract – Face play an important role in identifying people. What really appreciates the Researchers has the ability to recognize faces. Thousands of people recognize faces, and although visual stimuli have undergone major changes, it is important to identify familiar faces, conditions, expressions, ages, genders, and problems, such as eyeglasses or hairstyle changes. Many recent events, such as terrorist attacks, have shown that the most modern security systems are seriously flawed. Various government agencies are now encouraged to improve their safety data systems based on physical or behavioural characteristics, often referred to as biometric sources. Face Detection (FD) is a basic biometric feature that focuses on developing multiple algorithms to implement a variety of systems. FDD is one of the basic technologies that support Human-computer Interaction (HCI). Therefore, FD face alignment, face verification/authentication, gender/age recognition and face recognition (FR) are the main steps of all algorithmic algorithms. This paper aims to propose a new terrorism detection algorithm. Various changes and face detection functions can be suggested. This goal is implemented at different stages and uses different proposed algorithms. First, a powerful advanced template consisting of algorithms, the current technology perspective used in the current technology is used to detect facial features, and the other is contrary to the point of view, at least training can be done in real time. Photo- based approach finally, some samples are used to control whether a sample is encountered. The results of the rich research show that the method has a wide application range, strong lighting conditions, strong pirates and a wide variety. These results come from three different facial databases. The proposed program is implemented using MATLAB software and applies the algorithm cascaded object detector and finds the correct detection rate reaches 90% for detection.

Keyword: Face Detection (FD), MATLAB, Advance Template matching algorithms, Cascade Object Detector


    Facial recognition is a visual pattern recognition task. The three-dimensional human face, which is subject to varying illumination, pose, expression etc. has to be recognized. Analysis of Face recognition recognition can be performed on a variety of input data sources such as:

    • A single 2D image.

    • Stereo 2D images (two or more 2D images).

    • 3D laser scans.

    Also, soon Time of Flight (TOF) 3D cameras will be accurate enough to be used as well. The resolution of these sources can be increased by one by the inclusion of a time dimension. A still image with a time dimension is a video sequence. The

    advantage is that the identification of a person can be determined more precisely from a video sequence than from a picture since the identity of a person cannot change from two frames taken in sequence from a video sequence.The study is constrained to face recognition from single 2D images even when tracking of faces is done in video sequences.

    1.1 Importance of face detection

    Face detection plays an important role in todays world. It has many real-world applications like human/computer interface, surveillance, authentication and video indexing. However research in this field is still new Face recognition depends strictly on the particular choice of features used by the classifier. One usually starts with a given set of features and then attempts to derive a optimal subset (under some criteria) of features leading to high classification performance with the expectation that similar performance can also be displayed on future trials using novel (unseen) test data. Interactive Face Recognition (IFR) can benefit the areas of: Law Enforcement, Airport Security, Access Control, Driver's Licenses & Passports, Homeland Defence, Customs & Immigration and Scene Analysis. Study deals with the; in turn Law Enforcement Today's law enforcement agencies are looking for innovative technologies to help them stay one step ahead of the world's ever-advancing terrorists.


    Much of the work in computer recognition of faces has focused on detecting individual features such as the eyes, nose, mouth, and head outline, and defining a face model by the position, size, and relationships among these features. Such approaches have proven difficult to extend to multiple views and have often been quite fragile, requiring a good initial guess to guide them. Different research works performed in face recognition in recent time are explained below.

    M. Bicego et.al. [8] wrote a paper Using Hidden Markov Models and Wavelets for face recognition. In this paper, a new system for face recognition was proposed, based on Hidden Markov Models (HMMs) and wavelet coding. A sequence of overlapping sub-images is extracted from each face image, computing the wavelet coefficients for each of them. The whole sequence is then modelled by using Hidden Markov Models. The proposed method is compared with a DCT coefficients-based approach, showing comparable results.

    Liton Chandra Paul et.al. [9] wrote a paper Face Recognition Using Principal Component Analysis Method. This paper mainly addressed the building of face recognition system by using Principal Component Analysis (PCA). PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigen faces. These eigenvectors are obtained from covariance matrix of a training image set. The weights are found out after selecting a set of most relevant Eigen faces. Recognition is performed by projecting a test image onto the subspace spanned by the eigen faces and then classification is done by measuring minimum Euclidean distance.

    Divya Raj Singh N. Parmar et.al. [10] wrote a paper Face Recognition Methods & Applications. They described that Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.

    Issam Dagher et.al. [11] wrote a paper Face Recognition using the most Representative Sift Images. In this paper, face recognition using the most representative SIFT images was presented. It is based on obtaining the SIFT (Scale Invariant Feature Transform) features in different regions of each training image. Those regions were obtained using the K-means clustering algorithm applied on the key-points obtained from the SIFT algorithm. Based on these features, an algorithm which will get the most representative images of each face is presented. In the test phase, an unknown face image is recognizedaccording to those representative images. In order to show its effectiveness this algorithm is compared to other SIFT algorithms and to the LDP algorithm for different databases.

    G. Hemalatha et.al. [12] wrote a paper A Study of Techniques for Facial Detection and Expression Classification. In this paper they described the various approaches for facial recognition are categorized into two namely holistic based facial recognition and feature based facial recognition. Holistic based treat the image data as one entity without isolating different region in the face where as feature based methods identify certain points on the face such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various methods of facial detection, facial feature extraction and classification.

    Sarabjit Singh et.al. [13] wrote a paper A Face Recognition Technique using Local Binary Pattern Method. In this paper they described that LBP is really a very powerful method to explain the texture and model of a digital image. Therefore it was ideal for feature extraction in face recognition systems.

    A face image is first split into small regions that LBP histograms are extracted and then concatenated in to a single feature vector. This vector forms an efficient representation of the face area and can be used to measure similarities between images. Automatic facial expression analysis is a fascinating and challenging problem, and impacts important applications in several areas such as humancomputer interaction and data-driven animation.

  3. APPLICATIONS OF FACE RECOGNITION SYSTEM Face recognition is also useful in human computer interaction, virtual reality, database recovery, multimedia, computer entertainment, information security e.g. operating system, medical records, online banking., Biometric e.g. Personal Identification – Passports, driver licenses , Automated identity verification – border controls , Law enforcement e.g. video surveillances , investigation , Personal Security – driver monitoring system, home video surveillance system.

    Face Identification: Face recognition systems identify people by their face images. Face recognition systems establish the presence of an authorized person rather than just checking whether a valid identification (ID) or key is being used or whether the user knows the secret personal identification numbers (Pins) or passwords. The following are example.

    To eliminate duplicates in a nationwide voter registration system because there are cases where the same person was assigned more than one identification number. The face recognition system directly compares the face images of the voters and does not use ID numbers to differentiate one from the others. When the top two matched faces are highly similar to the query face image, manual review is required to make sure they are indeed different persons so as to eliminate duplicates.

    Access Control: In many of the access control applications, such as office access or computer logon, the size of the group of people that need to be recognized is relatively small. The face pictures are also caught under natural conditions, such as frontal faces and indoor illumination. The face recognition system of this application can achieve high accuracy without much co-operation from user. The following are the example.

    Face recognition technology is used to monitor continuously who is in front of a computer terminal. It allows the user to leave the terminal without closing files and logging out. When the user leaves for a predetermined time, a screen saver covers up the work and disables the mouse & keyboard. When the user comes back and is recognized, the screen saver clears and the previous session appears as it was left. Any other user who tries to logon without authorization is denied.

    Security: Today more than ever, security is a primary concern at airports and for airline staff office and passengers. Airport protection systems that use face recognition technology have been implemented at many airports around the world. The following are the two examples.

    In October, 2001, Fresno Yosemite International (FYI)

    SAD(x, y) = .

    airport in California deployed Visage's face recognition



    ( + , + , , )

    technology for airport security purposes. The system is designed to alert FYs airport public safety officers whenever an individual matching the appearance of a known terrorist suspect enters the airport's security checkpoint. Anyone recognized by the system would have further investigative

    processes by public safety officers. Computer security has also seen the application of face recognition technology. To

    The mathematical representation of the idea about looping

    through the pixels in the search image as we translate the origin of the template at every pixel and take the SAD measure is the following:

    (, )

    prevent someone else from changing files or transacting with



    others when the authorized individual leaves the computer

    terminal for a short time, users are continuously authenticated, checking that the individual in front of the computer screen or at a user is the same authorized person who logged in.

    Image database investigations: Searching image databases of licensed drivers, benefit recipients, missing children, immigrants and police bookings.

    General identity verification: Electoral registration, banking, electronic commerce, identifying newborns, national IDs, passports, employee IDs.

    Surveillance: Like security applications in public places, surveillance by face recognition systems has a low user satisfaction level, if not lower. Free lighting conditions, face orientations and other divisors all make the deployment of face recognition systems for large scale surveillance a challenging task. The following are some example of face- based surveillance. To enhance town center surveillance in Newham Borough of London, this has 300 cameras linked to the closed circuit TV (CCTV) controller room. The city council claims that the technology has helped to achieve a 34% drop in crime since its facility. Similar systems are in place in Birmingham, England. In 1999, Visions was awarded a contract from National Institute of Justice to develop smart CCTV technology.


      1. Advance Template matching algorithms

        Cross correlation is a template matching algorithm that estimates the correlation between two shapes that have a similar orientation and scale.

        For example, one way to handle translation problems on images, using template matching is to compare the intensities of the pixels, using the SAD (Sum of absolute differences) measure. A pixel in the search image with coordinates (xs, ys) has intensity Is (xs, ys) and a pixel in the template with coordinates (xt, yt) has intensity It (xt, yt). Thus the absolute difference in the pixel intensities is defined as Diff (xs, ys, x t, y t) = | Is (xs, ys) It(x t, y t) |.

        S rows and S cols denote the rows and the columns of the search image and T rows and T cols denote the rows and the columns of the template image, respectively. In this method the lowest SAD score gives the estimate for the best position of template within the search image.

      2. Cascade Object Detector: The cascade object detector uses the Viola-Jones algorithm to detect peoples faces, noses, eyes, mouth, or upper body. You can also use the Image Labeler to train a custom classifier to use with this System object.

      3. Simulation Result

    Fig 1. Basic GUI designed in MATLAB

    Fig 2. The image fetch from the folder

    Fig 3. Highlighted face of each in group image after the press button of detect image.


Face recognition is a biometric system used to identify or verify a person from a digital imae. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. In this paper we have studied literature review of various face recognition system along with their application areas.


[1] R. Jafri, H. R. Arabnia, A Survey of Face Recognition Techniques, Journal of Information Processing Systems, Vol.5, No.2, June 2009.

[2] C. A. Hansen, Face Recognition, Institute for Computer Science University of Tromso, Norway.

[3] M. D. Kelly. Visual identification of people by computer. PhD thesis, Stanford University, Stanford, CA, USA, 1971.

[4] T. Kanade. Computer Recognition of Human Faces, 47, 1977. [5] W. Zhao, R. Chellappa, P. J. Phillips & A. Rosenfeld, Face recognitions literature survey, ACM Computing Surveys, Vol.

35, No. 4, December 2003, pp. 399458.

[6] C. Gonzalez, R. E. Woods, S. liddins, "Digital Image processing Using MATLAB".

[7] S. Suhas, A. Kurhe, Dr. P. Khanale, Face Recognition Using Principal Component Analysis and Linear Discriminant Analysis on Holistic Approach in Facial Images Database, IOSR Journal of Engineering, Vol. 2, Issue 12 (Dec. 2012), PP 15-23

[8] M. Bicego, U. Castellani, V. Murino, Using Hidden Markov Models and Wavelets for face recognition, Proceedings of the 12th International Conference on Image Analysis and Processing (ICIAP03) © 2003 IEEE.

[9] Liton Chandra Paul, Abdulla Al Sumam, Face Recognition Using Principal Component Analysis Method, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 1, Issue 9, November 2012.

[10] Divya Raj Singh N. Parmar, Brijesh B. Mehta, Face Recognition Methods & Applications, International Journal of Computer Technology & Applications, Vol. 4 Jan-Feb 2013.

[11] Issam Dagher, Nour El Sallak and Hani Hazim, Face Recognition using the most Representative Sift Images, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7, No.1 (2014), pp.225-236

[12] G. Hemalatha, C.P. Sumathi, A Study of Techniques for Facial Detection and Expression Classification, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.2, April 2014.

[13] Sarabjit Singh, Amrit Pal Kaur, Taqdir, A Face Recognition Technique using Local Binary Pattern Method, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 3, March 2015.

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