In the article, Facial recognition software helps convict a robber, Jon Fingas states that Pierre Martin was convicted of armed robbery after a facial recognition software was used to “match surveillance camera footage with an existing mugshot". This is a logical scenario. First, the author points out that investigators would most likely have not going through 4.5 million photos. For example, doing this would take too much time. Having a software to do that job just as efficiently and quickly as
A Robust Object Recognition using LBP, LTP and RLBP Nithya.K1, Karthi Prem.S2 and Udhayachandrika.A3 1Department of IT, Vivekanandha College of Engineering for Women, nithuthaara91 @gmailcom 2Department of IT, Vivekanandha College of Engineering for Women, karthiprem @gmail.com 3Department of IT, Vivekanandha College of Engineering for Women, udhayaa11 @gmail.com Abstract— In this paper two set of edge-texture features is proposed such as Discriminative Robust Local Binary Pattern
A Strong Object Recognition is Implemented by LBP, LTP and RLBP vany.s PG Scholar, Department of Information Technology Vivekanandha College of Engineering for Women (Autonomous), Tamilnadu Tiruchengode, India s.vany089@gmail.com Karthi Prem. S Assistant Professor, Department of Information Technology Vivekanandha College of Engineering for Women (Autonomous), Tamilnadu Tiruchengode, India karthiprem@gmail.com Udhayachandrika. A Assistant Professor, Department of Information Technology Vivekanandha
Mel cepstral feature extraction technique is required in some or the other form in most of the latest speech and speaker recognition system. Here, first samples of speech are splitted into overlapping frames. Generally the length of frame is 25 ms and frame rate is 10 ms. Each and every frames are refined by pre-emphasis filter which amplifies higher frequencies. Next is to apply windowing so that Fourier spectrum for each windowing frame is achieved here Hamming window is used. To obtain vector
Occlusion the performance of the face recognition algorithms under occlusion is in general poor. The face may be occluded by other objects in the scene or by sunglasses or other things. Occlusion may be unintentional or intentional. Under some conditions subjects may be motivated to thwart recognition efforts by covering portions of their face. Since in many situations, the goal is to recognize none or even un-cooperating subjects. Time delay Faces change over time. There are changes in hair style
RFID AND FACE RECOGNITION BASED ACCESS CONTROL SYSTEM 1Kenward Dzvifu, 2T Chakavarika Department of Information Security & Assurance, Harare Institute of Technology, Zimbabwe 1kenwarddzvifu@gmail.com 2ttchaka@gmail.com School of Information Science and Technology, Harare Institute of Technology, Zimbabwe ABSTRACT— The Radio frequency identification (RFID) technology has been broadly adopted in access control systems. This technology is based on the use of a card or tag and has some major
In this paper we present an analysis of face recognition system with a combination of Neural networks withSub-space method of feature extraction. Here we are considering both single layer such as Generalized Regression neural network (GRNN) and Multi layer such as Learning Vector quantization (LVQ). The analysis of these neural networks are done between feature vectors with respect to the recognition performance of subspace methods, namely Principal Component Analysis (PCA) and Fisher Linear Discriminant
for automatic detection and recognition of student during academics, followed by display of personal information of students. This application makes proper use of CCTV camera for real time face detection of students of particular college. The proposed application can be divided into four major steps. In first step, each person in the image is detected. In the second step, a face detection algorithm detects faces of each person. In third step, we use a face recognition algorithm to match the faces
for automatic detection and recognition of student during academics, followed by display of personal information of students. This application makes proper use of CCTV camera for real time face detection of students of particular college. The proposed application can be divided into four major steps. In first step, each person in the image is detected. In the second step, a face detection algorithm detects faces of each person. In third step, we use a face recognition algorithm to match the faces
Ear Recognition Ear images can be acquired in a similar manner to face images, and a number of researchers have suggested that the human ear is unique enough to each individual to allow practical use as a biometric. The Alfred system presented by the researchers (Zhang. K., et. al., 2006) suggests that for identity recognition- ear shape can be used as a unique and comparable feature. The objective of this system was to extract ear shape features for recognition. Active shape model is used to model