Face recognition and detection is one of the most important fields of the modern applications. Face recognition system uses two subsystems named face detection system and image database system. Face recognition can be of feature based and image based. Feature based method uses features like skin color, eyes, nose and mouth to detect and recognize human face whereas image based method utilizes some preprocessed image sets for detection. The project implements feature based face recognition system which first finds any face or faces in the color image and then matches it against the database to recognize the individuals. Here, the skin color pixels are used to filter out the interesting regions of human skin from other non interesting regions. Once the skin regions are located, facial features like mouth, eyebrow and nose are extracted to locate the human face. Then, the detected face from image will be compared with the database of training images to find a match. The project is implemented using Visual Basic and Microsoft Access for database management.



Face recognition system is an application for identifying someone from image or videos. Face recognition is classified into three stages i.e. Face detection, Feature Extraction ,Face Recognition. Face detection method is a difficult task in image analysis. Face detection is an application for detecting object, analyzing the face, understanding the localization of the face and face recognition. It is used in many application for new communication interface, security etc.Face Detection is employed for detecting faces from image or from videos. The main goal of face detection is to detect human faces from different images or videos. The face detection algorithm converts the input images from a camera to binary pattern and therefore the face location candidates using the AdaBoost Algorithm. The proposed system explains regarding the face detection based system on AdaBoost Algorithm. AdaBoost Algorithm selects the best set of Haar features and implement in cascade to decrease the detection time .The proposed System for face detection is intended by using Verilog and ModelSim, and also implemented in FPGA.

Face Detection System is to detect the face from image or videos. To detect the face from video or image is gigantic. In face recognition system the face detection is the primary stage. Figure 1 shows the various stages of face recognition system ie face detection, feature extraction and recognition. Now Face Detection is in vital progress in the real world

Face recognition is a pattern recognition technique and one of the most important biometrics; it is used in a broad spectrum of applications. The accuracy is not a major problem that specifies the performance of automatic face recognition system alone, the time factor is also considered a major factor in real time environments. Recent architecture of the computer system can be employed to solve the time problem, this architecture represented by multicore CPUs and manycore GPUs that provide the possibility to perform various tasks by parallel processing. However, harnessing the current advancements in computer architecture is not without difficulties. Motivated by such challenge, this research proposes a Face Detection and Recognition System FDRS. In doing so, this research work provides the architectural design, detailed design, and four variant implementations of the FDRS.


Face recognition has gained substantial attention over in past decades due to its increasing demand in security applications like video surveillance and biometric surveillance. Modern facilities like hospitals, airports, banks and many more another organizations are being equipped with security systems including face recognition capability. Despite of current success, there is still an ongoing research in this field to make facial recognition system faster and accurate. The accuracy of any face recognition system strongly depends on the face detection system. The stronger the face detection system the better the recognition system would be. A face detection system can successfully detect human face from a given image containing face/faces and from live video involving human presence. The main methods used in these days for face detection are feature based and image based. Feature based method separates human features like skin color and facial features whereas image based method used some face patterns and processed training images to distinguish between face and non faces. Feature based method has been chosen because it is faster than image based method and its implementation is far more simplified. Face detection from an image is achieved through image processing. Locating the faces from images is not a trivial task; because images not just contain human faces but also nonface objects in clutter scenes. Moreover, there are other issues in face recognition like lighting conditions, face orientations and skin colors. Due to these reasons, the accuracy of any face recognition system cannot be 100.

Face recognition is one of the most important biometrics methods. Despite the fact that there are more reliable biometric recognition techniques such as fingerprint and iris recognition, these techniques are intrusive and their success depends highly on user cooperation. Therefore, face recognition seems to be the most universal, nonintrusive, and accessible system. It is easy to use, can be used efficiently for mass scanning, which is quite difficult, in case of other biometrics . Also it is natural and socially accepted.

Moreover, technologies that require multiple individuals to use the same equipment to capture their biological characteristics probably expose the user to the transmission of germs and impurities from other users. However, face recognition is completely nonintrusive and does not carry any such health dangers.

Biometrics is a rapidly developing branch of information technology. Biometric technologies are automated methods and means for identification based on biological and behavioral characteristics of an individual. There are several advantages of biometric technologies compared to traditional identification methods. To take adequate measures against increasing security risks in modern world, countries are considering these advantages and are shifting to new generation identification systems based on biometric technologies.


Biometric systems are becoming an important element gateway for information security systems. Therefore biometric systems themselves have to satisfy high security requirements. Unfortunately producers of biometric technologies do not always consider security precautions. In publications regarding biometric technologies, drawbacks and weaknesses of these technologies have been discussed. Since biometrics form the technology basis for large scale and very sensitive identification systems e.g. passports, identification cards, the problem of adequate evaluation of the security of biometric technologies is a current issue.

Also, some other issues with face detection and recognition system is on individual with identical face like identical twins and others, in situation like this it is possible for the system to make mistake or error in processing the person image so as to grant access to the rightful user.


The objective of this project is to implement a face recognition system which first detects the faces present in either single image frames; and then identifies the particular person by comparing the detected face with image database or in the both image frames.

In addition to the main objective of this research work, the researcher also went far more to add other features to the new system which are as fellow.

1.One of the objectives of this system is to design a system that will help the organization maintain a strong security in the work environment.

2.Highlight areas of vulnerability in the new system

3.Develop a ridged and secure database for the organization to enable them secure their sensitive data and records.


This study is primarily aimed at increasing efficiency in security, this research work will help the users in maintaining data. This system will reduce the rate of fraudulent activities as it can as well keep track of registered users and grant them access upon face recognition completion.

Also the knowledge that would be obtained from this research will assist the management to grow, also this research work will also be of help to the upcoming researcher in this field of study both with the academic students on their study.

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