Debotosh Bhattacharjee

Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition

In this paper, we present an OCR for handwritten Devnagari characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain code histogram features and line fitting features are computed by dividing the character image into different segments. Weighted majority voting technique is used for combining the classification decision obtained from four multi layer perceptron(MLP) based classifier. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.80% as we considered top five choices results. This method is compared with other recent methods for handwritten Devnagari character recognition and it has been observed that this approach has better success rate than other methods.

Human face recognition using fuzzy multilayer perceptron

In this work a novel method for human face recognition that is based on fuzzy neural network has been presented. Here, Gabor wavelet transformation is used for extraction of features from face images as it deals with images in spatial as well as in frequency domain to capture different local orientations and scales efficiently. In face recognition problem multilayer perceptron (MLP) has already been adopted owing to its efficiency, but it does not capture overlapping and nonlinear manifolds of faces which exhibit different variations in illumination, expression, pose, etc. A fuzzy MLP on the other hand performs better than an MLP because fuzzy MLP can identify decision surfaces in case of nonlinear overlapping classes, whereas an MLP is restricted to crisp boundaries only. In the present work, a new approach for fuzzification of the feature sets obtained through Gabor wavelet transforms has been discussed. The feature vectors thus obtained are classified using a newly designed fuzzified MLP. The system has been tested on a composite database (DB-C) consisting of the ORL face database and another face database created for this purpose and a recognition rate of 97.875% with fuzzy MLP against a recognition rate of only 81.25% with MLP whose feature vectors were also obtained through same Gabor wavelet transforms has been obtained.

Comparative study of human thermal face recognition based on Haar wavelet transform and local binary pattern

Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each of size 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database.

Thermal Infrared Face Recognition – A Biometric Identification Technique for Robust Security system

Face of an individual is a biometric trait that can be used in computer-based automatic security system for identification or authentication of that individual. While recognizing a face through a machine, the main challenge is to accurately match the input human face with the face image of the same person already stored in the face-database of the system. Not only the computer scientists, but the neuroscientists and psychologists are also taking their interests in the field of development and improvement of face recognition. Numerous applications of it relate mainly to the field of security. Having so many applications of this interesting area, there are challenges as well as pros and cons of the systems. Face image of a subject is the basic input of any face recognition system. Face images may be of different types like visual, thermal, sketch and fused images. A face recognition system suffers from some typical problems. Say for example, visual images result in poor performance with illumination variations, such as indoor and outdoor lighting conditions, low lighting, poses, aging, disguise etc. So, the main aim is to tackle all these problems to give an accurate automatic face recognition. These problems can be solved using thermal images and also using fused images of visual and thermal images. The image produced by employing fusion method provides the combined information of both the visual and thermal images and thus provides more detailed and reliable information which helps in constructing more efficient face recognition system. Objective of this chapter is to introduce the role of different IR spectrums, their applications, some interesting critical observations, available thermal databases, review works, some experimental results on thermal faces as well as on fused faces of visual and thermal face images in face recognition field; and finally sorting their limitations out.

Local-Gravity-Face ( LG-face ) for Illumination-Invariant and Heterogeneous Face Recognition

This paper proposes a novel method called local-gravity-face (LG-face) for illumination-invariant and heterogeneous face recognition (HFR). LG-face employs a concept called the local gravitational force angle (LGFA). The LGFA is the direction of the gravitational force that the center pixel exerts on the other pixels within a local neighborhood. A theoretical analysis shows that the LGFA is an illumination-invariant feature, considering only the reflectance part of the local texture effect of the neighboring pixels. It also preserves edge information. Rank 1 recognition rates of 97.78% on the CMU-PIE database and 97.31% on the Extended Yale B database are achieved under varying illumination, demonstrating that LG-face is an effective method of illumination-invariant face recognition. For HFR, when faces appear in different modalities, LG-face produces a common feature representation. Rank 1 recognition rates of 99.96% on the CUFS database, 98.67% on the CUFSF database, and 99.78% on the CASIA-HFB database show that the LG-face is also an effective method for HFR. The proposed method also performs consistently in the presence of complicated variations and noise.

Geometric feature based face-sketch recognition

This paper presents a novel facial sketch image or face-sketch recognition approach based on facial feature extraction. To recognize a face-sketch, we have concentrated on a set of geometric face features like eyes, nose, eyebrows, lips, etc and their length and width ratio because it is difficult to match photos and sketches because they belong to two different modalities. In this system, first the facial features/components from training images are extracted, then ratios of length, width, and area etc. are calculated and those are stored as feature vectors for individual images. After that the mean feature vectors are computed and subtracted from each feature vector for centering of the feature vectors. In the next phase, feature vector for the incoming probe face-sketch is also computed in similar fashion. Here, K-NN classifier is used to recognize probe face-sketch. It is experimentally verified that the proposed method is robust against faces are in a frontal pose, with normal lighting and neutral expression and have no occlusions. The experiment has been conducted with 80 male and female face images from different face databases. It has useful applications for both law enforcement and digital entertainment.

Study of Different Features on Handwritten Devnagari Character

In this paper a scheme for offline Handwritten Devnagari Character Recognition is proposed, which uses different feature extraction and recognition algorithms. The proposed system assumes no constraints in writing style, size or variations. First the character is preprocessed and features namely : Chain code histogram, four side views, shadow based are extracted and fed to Multilayer Perceptrons as a preliminary recognition step. Finally the results of all MLPs are combined using weighted majority scheme. The proposed system is tested on 1500 handwritten devnagari character database collected from different people. It is observed that the proposed system achieves 98.16% recognition rates as top 5 results and 89.58% as top 1 results.

Construction of human faces from textual descriptions

The FAce SYnthesis (FASY) system described in this paper, presents a novel face construction approach based on textual description with the stored facial components extracted from different face databases. The system has two types of databases: (a) Full Face Database (DB-F) consisting of frontal face images collected from different face databases (b) Facial Component Database (DB-C) consisting of facial components extracted from the faces of DB-F. Both the databases also contain the textual description of the face images/facial component images. If the desired face as per the description of the user is not available in DB-F, then new face is constructed with the help of DB-C. The experiment has been conducted with 200 male and female face images from different face databases. The successful extraction of the facial components from the face images as mentioned above has been found to be 93% on an average and face construction satisfies the user’s textual query in 80% cases on an average. The work has been done using Visual Basic 6.0 and Matlab 6.5. The face construction method is implemented in VHDL, simulated by Modelsim SE/PE, and synthesized with Xilinx Webpack 4.1 followed by loading into the FPGA device.

Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition

This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 97.05%.

Adaptive polar transform and fusion for human face image processing and evaluation

Human face processing and evaluation is a problem due to variations in orientation, size, illumination, expression, and disguise. The goal of this work is threefold. First, we aim to show that the variant of polar transformation can be used to register face images against changes in pose and size. Second, implementation of fusion of thermal and visual face images in the wavelet domain to handle illumination and disguise and third, principal component analysis is applied in order to tackle changes due to expressions up to a particular extent of degrees. Finally, a multilayer perceptron has been used to classify the face image. Several techniques have been implemented here to depict an idea about improvement of results. Methods started from the simplest design, without registration; only combination of PCA and MLP as a method for dimensionality reduction and classification respectively to the range of adaptive polar registration, fusion in wavelet transform domain and final classification using MLP. A consistent increase in recognition performance has been observed. Experiments were conducted on two separate databases and results yielded are very much satisfactory for adaptive polar registration along with fusion of thermal and visual images in the wavelet domain.