Face Detection and Recognition System using Digital Image Processing

Face Detection and Recognition System using Digital Image Processing

Table of Contents


While recognizing any individual, the most important attribute is face. It serves as an individual identity of everyone and therefore face recognition helps in authenticating any person’s identity using his personal characteristics. The whole procedure for authenticating any face data is sub-divided into two phases, in the first phase, the face detection is done quickly except for those cases in which the object is placed quite far, followed by this the second phase is initiated in which the face is recognized as an individual. Then the whole process is repeated thereby helping in developing a face recognition model which is considered to be one of the most extremely deliberated biometric technology. Basically, there are two types of techniques that are currently being followed in the face recognition pattern that is, the Eigenface method and the Fisherface method. The Eigenface method basically makes use of the PCA (Principal Component Analysis) to minimize the face dimensional space of the facial features. The area of concern of this paper is using digital image processing to develop a face recognition system.


  • Author Keywords

    • Face recognition system,
    • eigenface method,
    • principal component analysis (PCA),
    • digital image processing
  • IEEE Keywords

    • Face,
    • Face recognition,
    • Face detection,
    • Principal component analysis,
    • Neural networks,
    • Image color analysis,
    • Lighting


After the face is detected the main task of the face recognition system starts to identify the known or unknown face and act accordingly. Often people are mistaken by the term face detection whereas face recognition means on the other hand is to authenticate a given face data based on the stored face data in the database. Once the face data matches the database then the system is authenticated [1] [2]. There are different approaches that are being followed in the whole process of face recognition. Each process has its own pros and cons and has also some limitations which make it different from other approaches [5].


There are basically two prevailing approaches to the problem of face recognition namely, the Geometric approach i.e. the feature-based, and the other one is the photometric approach i.e. the view based. As the field of face recognition fascinated many researchers resulting which there were many contrasting algorithms developed, out of which three of them have been widely studied in the literature of face recognition[3][4]. We can classify the recognition algorithm into two main approaches:

1) Geometric: This approach mainly deals with the spatial correlation uniting the profile (i.e. face) features, also we can simply that dimensional layout of the facial attributes. Some of the main geometrical attributes of a human face are nose, eyes, and mouth. Based on these attributes firstly the face is categorized and then based on these attributes respective spatial intervals and the respectively associated gradients are estimated, thereby advancing the process of face recognition.

2) Photometric stereo: It is a methodology of computer vision technology that mainly recuperates the structure of an underlying object from the images that were shot in varying circumstances that were affected by the lighting environment [7]. An arrangement of the surface standards shown by the slope chart that finally

3) Localization: After the process of classification, the bounding box is thus used to localize the searched human face from the results of the trained neural network. There are numerous attributes of the face on which work has been done, some of them are: Position, scale, orientation, and illumination.

Till now we have seen how an image is processed and how it is being checked for the presence of any face or not. Now, we will talk more about face detection. After the facial attributes are detected in any image all the rest of the objects are ignored and our primary concern is with the face. Face detection can be also referred to as face localization, where the main aim is to find the location and size of the known no. of faces present in an image. Primarily there are two approaches that are being followed to recognize the facial part in an image, the first approach is the feature base approach and the second approach is the image-based approach [6][8][10].


Since the no. of Eigenfaces to be used is restricted in PCA transformation that’s why the system did not have an accuracy of more than 90% for both manual and automatic face recognition. Further work that needs to be done is in the field of fully automated frontal view face detection system which when displayed virtually shows perfect accuracy. The real-world performance of this designed system will be far more precise. In view of attaining a high accuracy rate the designed and developed system was not adequately strong. One of the main reasons behind this flaw is that the sub-system of the face recognition system does not exhibit minute changes in the degree of steadiness to scale or rotation of the segmented face image. The performance of this system can be compared with the manual face detection only if we integrate the eye detection system with the developed system. The other executed applications exhibited an exceptional result and returned exceptionally good on the PCA technique and distorted arrangement. Appropriate use of this developed system of face recognition and detection is in the field of surveillance and mugshot matching. This system after integration with the eye detection system could mainly serve as an authentication system in the ATM Machines and Home security systems. There are many advancements possible in this field as this serves as an insight into what the future will be in the field of computer vision.

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FULL Paper PDF file:

Face Detection and Recognition System using Digital Image Processing



G. Singh and A. K. Goel,




Face Detection and Recognition System using Digital Image Processing

Publish in

,2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, India, 2020, pp. 348-352



PDF reference and original file: Click here



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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

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Maryam kakaei was born in 1984 in Arak. She holds a Master's degree in Software Engineering from Azad University of Arak.