Automated thermal face recognition based on minutiae extraction

Abstract

In this paper an efficient approach for human face recognition based on the use of minutiae points in thermal face image is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing methods are used to pre-process the captured thermogram, from which different physiological features based on blood perfusion data are extracted. Blood perfusion data are related to distribution of blood vessels under the face skin. In the present work, three different methods have been used to get the blood perfusion image, namely bit-plane slicing and medial axis transform, morphological erosion and medial axis transform, sobel edge operators. Distribution of blood vessels is unique for each person and a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. Two different methods are discussed for extracting minutiae points from blood perfusion data. For extraction of features entire face image is partitioned into equal size blocks and the total number of minutiae points from each block is computed to construct final feature vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. A five layer feed-forward back propagation neural network is used as the classification tool. A number of experiments were conducted to evaluate the performance of the proposed face recognition methodologies with varying block size on the database created at our own laboratory. It has been found that the first method supercedes the other two producing an accuracy of 97.62% with block size 16×16 for bit-plane 4.

Bio-Inspired Presentation Attack Detection for Face Biometrics

“…With the aid of infrared sensors authors in Prokoski and Riedel (2002) analyzed facial thermograms for rapid, and varied illumination environments. Similar thermography methods were presented in Hermosilla et al (2012) and Seal et al (2013). Motion-based techniques are mostly employed in video sequences to detect motion anomalies between frames.…”

Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks

“…Mainly two steps, namely, feature extraction and classification, are associated with the FER task. Conventional features, such as Gobar wavelets [4], curves [12], scale-invariant feature transform [21], HOG [8], LBP [6], minutiae points [11], Haar wavelet [5], HBIV [9], DBN [10], and edges [38], were exploited with advanced domain comprehension in the first step. In the second step, support vector machine (SVM) [39], feedforward neural network [40], and extreme learning machine [41] were adopted for classification.…”

“…α j P i j c (11) where c indicates the various expressions and that i represents the input sample and N indicates that the two different modalities, in this study M and D of the input image, are considered as two different modalities. P i j c could be a prediction probability to belong to a class c for input sample i of modality j .…”

“…Examples include Gobar wavelets [4], Haar wavelet [5], local binary pattern (LBP) [6], [7], histogram of oriented gradients (HOG) [7], [8], histogram of bunched intensity values (HBIV) [9], dynamic Bayesian network (DBN) [10], and so on. On the other hand, geometric features are obtained by transforming the image into geometric primitives, such as corner or minutiae points [11], edges, and curves [12]. This is accomplished, for example, by locating unique features, such as eyes, mouth, nose, and chin, and measuring their relative position [13], [14], width, and, perhaps, other parameters.…”

Improving the generalization capability of face spoofing detection

“…Another example is to utilize a specific LED and photodiodes with difference wavelengths [58] [59] which can measure the reflectance information as the discriminative feature for face spoofing detection. We can also leverage other sensors which were already proved to be effective for face recognition task, including facial vein detection sensor [60], 3D…”