The main objective of this project is to develop face recognition based automated student attendance system. The test images and training images have to be captured by using the same device to ensure no quality difference. In addition, the students have to register in the database to be recognized. The enrolment can be done on the spot through the user-friendly interface. The making of automatic attendance system involves some crucial steps and these steps are. Face detection Data collection Face recognition Automatic attendance system
Difference between face detection and face recognition are often misunderstood. Face detection is to determine only the face segment or face region from image, whereas face recognition is to identify the owner of the facial image. In our case we have used the inbuilt toolbox of matlab that is computer vision tool box along with image processing and acquisition toolbox to capture the image in every second and in this process the images are converted from rgb to grey scale image for extraction of feature and then detection of face in the image. We can also use this toolbox in detection of other types of different object. The command to use the toolbox and its feature that is face detection is vision.cascadeobjectdetector. Data collection plays an essential role to improve the accuracy of face recognition. Scaling is of image is part of data collection and the important preprocessing steps to manipulate the size of the image. Scaling down of an image increases the processing speed by reducing the system computations since the number of pixels are reduced. The size and pixels of the image carry spatial information. The size should be same for all the images for normalization and standardization purposes. To extract features from facial images, same length and width of image is preferred, thus images were scaled to 120 × 120 pixels. Besides scaling of images, colour image is usually converted to grayscale image for pre-processing. Grayscale images are believed to be less sensitive to illumination condition and take less computational time. Grayscale image is 8 bit image which the pixel range from 0 to 255 whereas colour image is 24 bit image which pixel can have 16 77 7216 values. Hence, colour image requires more storage space and more computational power compared to grayscale images. If colour image is not necessary in computation, then it is considered as noise. Face Recognition Technique (FRT) can only recognize a face if a specific individual face has alreadybeen added to the system in advance. Hence this step involves testing and training process through which we can distinguish between different faces.Thecondition of the enrolment and the quality of resulting image have significant impact on the finalefficiency of FRT. In the process of testing we have used one of the toolbox from MATLAB that is Deep learning toolbox model for akexnet. Basically this toolbox stores all the features of the trained database in the form of numerical digits these digits are extracted data of images in the form of pixels. Now how face recognition works, Facial recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If you look at the mirror, you can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features. These landmarks are defined as nodal points. There are about 80 nodal points on a human face. Some of them are:- Distance between the eyes Width of the nose Depth of the eye socket Cheekbones Jaw line Chin The last step involves the marking of attendance after the system recognises the face. The attendance will going to be marked on an excel sheet by using the inbuilt function of MATLAB that is xlswrite(‘foldername’,data, , ‘range of sheet’). In this proposed approach, face recognition student attendance system with userfriendly interface is designed by using MATLAB. With the help of each sophisticated code each provides specific function, for example, detection code simply shows the process of detection and data collection simply collects the data of new user. Lastly the main attendance code in which by simply running it we can first recognise the face and then if face is recognised it automatically mark attendance in the excel sheet.