An Efficient Method for Face Localization and Recognition in Color Images

Pseudo-Zernike moments have better feature representation capability, and are more robust to image noise than those of the conventional Zernike moments. However, due to the computation complexity of pseudo-Zernike polynomials, pseudo-Zernike moments are yet to be extensively used as feature descriptors as compared to Zernike moments. In this paper, we propose two new algorithms, namely coefficient method and p-recursive method, to accelerate the computation of pseudo-Zernike moments. Coefficient method calculates polynomial coefficients recursively. It eliminates the need of using factorial functions. Individual order or index of pseudo-Zernike moments can be derived independently, which is useful if selected orders or indices of moments are needed as pattern features. p-recursive method uses a combination of lower order polynomials to derive higher order polynomials with the same index q. Fast computation is achieved because it eliminates the requirements of calculating polynomial coefficients, Bpqk, and power of radius, rk, in each polynomial. The performance of the proposed algorithms on moment computation and image reconstruction, as compared to those of the present methods, are experimentally verified using a set of binary and grayscale images.

This paper pertains to the recognition of human face within the color images. The proposed scheme used the clue of skin color to detect faces within the color images, and Mallat wavelet transform was applied to get the LL2 frequency subband image of the original faces, then, features were extracted from the raw data by using principal component analysis (PCA). These discriminative and low dimensional features achieved were fed to a support vector machines (SVMs) with polynomial kernel for the training and fuzzy theory was applied on multi-class classification to deal with the unclassifiable cases. Experiments were conducted using the indoors photographs and the results demonstrated that the proposed methodology was efficient for the frontal faces detection and recognition.

Ladar range images have attracted considerable attention in automatic target recognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples.

This paper presents the use of Hidden Markov Models (HMM) to classifying Arabic words that have been represented by moments into a set of predefined classes. The proposed scheme results in enhanced performance because it deals effectively with errors that commonly originate during the feature extraction stage and it accounts for variations due to the individual handwriting performance of different data sets. To overcome the variations in images, each is represented by rotation and scale invariant feature vector known as Pseudo Zernike Moments (PZM). To demonstrate its effectiveness, the proposed classification scheme has been employed in the context of different sets of handwritten Arabic words. The classification results demonstrate that the proposed approach can classify handwritten Arabic word with a error rate less than 10%.

In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.

In this paper, we present a Face Recognition method based on the combination of the Gabor Wavelets (GW) and the Phase Congruency (PC) method [1]. While the GW method allows an efficient space-frequency analysis to code the Face Image points, it requires significant down-sampling, in order to end up with a reasonably small-size feature vector. Now, the Phase Congruency method can be used as an efficient features detector, to uncover the salient local features of the face and can be computed using the GW Transform. An additional advantage of the Phase Congruency method is its insensitivity to variations in image illumination and contrast. The method proposed is to use the PC method in determining the salient points of the Face Image, thus replacing the random down-sampling method. The first step in our proposed method is to obtain the local frequency information of a face image using a multi-scale, multi-orientation set of Gabor filters, from which the Phase Congruency image is computed. We then use this method to select a limited number of maximum-value points, whose values are taken from the GW- transformed face images, forming a feature vector of about 5000 components. Preliminary results using the ORL face dataset show 98% recognition rate even without the use of the PCA method. Upon using the PCA method we can further reduce the number of components to 45 while still retaining the recognition rate of 98%. This as compared to a recognition rate of 96% using GW and PCA with a random 64:1 down-sampling on the GW coefficients. The proposed combined method, allows in addition to an efficient reduction in the number of image points used, the derivation of both the PC image and the GW feature vector representation, using a single GW transform.

Assessment of cluster analysis of elastic light scatter profiles for the identification of foodborne Bacteria.

We present invariant image watermarking based on a recently introduced set of polar harmonic transforms and angular radial transforms and their comparative analysis with state-of-art approaches based on Zernike moments and pseudo-Zernike moments (ZMs/PZMs). Similar to ZMs/PZMs, these transforms provide rotation invariance and resilience to noise while mitigating inherent limitations like numerical instability and computational cost at high order of moments. These characteristics motivate us to design invariant transform-based invariant image watermarking schemes that can withstand various intentional or unintentional attacks, handle large bitcarriers, and work in a limited computing environment. A comparative performance evaluation of watermarking systems regarding critical parameters like visual imperceptibility, embedding capacity, and watermark robustness against geometric transformations, common signal processing distortions, and Stirmark attacks is performed along with the empirical analysis of various inherent properties of transforms and moments such as magnitude invariance, reconstruction capabilities, and computational complexity to investigate relationships between the performance of watermarking schemes and inherent properties of transforms.

Invariant image representation plays an important role in many pattern recognition applications, such as texture classification, face recognition and character recognition. In this paper, we evaluate some of the invariant orthogonal moments, including Zernike moment (ZM), pseudo-Zernike moment (PZM), and Polar Harmonic Transform (PHT), which are all computed in a circular domain. The performance of invariance and image representation ability are evaluated.

Face recognition is an interesting field of computer vision with many commercial and ?scientific applications. It is considered as a very hot topic and challenging problem at the ?moment. Many methods and techniques have been proposed and applied for this purpose, ?such as neural networks, PCA, Gabor filtering, etc. Each approach has its weaknesses as ?well as its points of strength. This paper introduces a highly efficient method for the ?recognition of human faces in digital images using a new feature extraction method that ?combines the global and local information in different views (poses) of facial images. ?Feature extraction techniques are applied on the images (faces) based on Zernike moments ?and structural similarity measure (SSIM) with local and semi-global blocks. Pre-processing ?is carried out whenever needed, and numbers of measurements are derived. More ?specifically, instead of the usual approach for applying statistics or structural methods ?only, the proposed methodology integrates higher-order representation patterns extracted ?by Zernike moments with a modified version of SSIM (M-SSIM). Individual measurements and metrics resulted from mixed SSIM and Zernike-based approaches give a powerful ?recognition tool with great results. Experiments reveal that correlative Zernike vectors give ?a better discriminant compared with using 2D correlation of the image itself. The ?recognition rate using ORL Database of Faces reaches 98.75%, while using FEI ??(Brazilian) Face Database we got 96.57%. The proposed approach is robust against ?rotation and noise.?

A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI

Facial recognition is a challenging research in the field of image processing and computer vision, especially for security systems, weight determiner, and emotional determination based on the face image recognition. Some of the methods that can be used in facial recognition are holistic, feature extraction, hybrids and intelligent systems. This paper used the method of characteristic extraction that used Principal Component Analysis (PCA) which was built using EmguCV application. The purpose of this research is to assess the accuracy of Principal Component Analysis (PCA) method when combined with Emgu CV in face recognition in real time. Based on the results of training and testing, it can be concluded that the PCA method combined with EmguCV has better accuracy, if the data used has the same characteristics, PCA and EmguCV can also be developed to make image processing application especially for security system, because it applies simple statistic method and easy- applied algorithm.

Building performance simulations (BPS) are essential for managing rising energy demands but are computationally intensive, limiting their use in early-stage design. Surrogate models (SMs) offer fast approximations using machine learning but lack generalizability for changing building morphologies. This study introduces three morphology-based feature extraction methods – Zernike image moments, polar raycasting, and principal component analysis – to train a generalizable SM for predicting annual energy use intensity (EUI) of 2.5D residential buildings in Singapore. The best SM achieved a 9.2% RMSPE, with features yielding low error and avoiding overfitting. This framework enables efficient design space exploration, aiding architects and urban designers in optimizing masterplans for energy efficiency. In the final section, we demonstrate a design space exploration (DSE) framework with our generalizable SM using a novel parametric model, showcasing its speed, adaptability and convenience over conventional optimization workflows. Highlights Novel approach explores building morphology's impact to annual EUI . Using Zernike moments, ray casting and PCA to extract features from any 2.5D buildings. 4000 tuned SMs trained from hyperparameter optimisation. Most robust SM achieved 9.2% RMSPE when tested with 800 samples. SM's generalizability is proven by applying it to a design model with previously unseen parameters.

Scene text presents challenging characteristics mainly related to acquisition circumstances and environmental changes resulting in low quality videos. In this paper, we present a scene text detection algorithm based on pseudo Zernike moments (PZMs) and stroke features from low resolution lecture videos. Algorithm mainly consists of three steps: slide detection, text detection and segmentation and non-text filtering. In lecture videos, slide region is a key object carrying almost all important information; hence slide region has to be extracted and segmented from other scene objects considered as background for later processing. Slide region detection and segmentation is done by applying pseudo Zernike moment's based on RGB frames. Text detection and extraction is performed using PZMs segmentation over V channel of HSV colour space, and then stroke feature is used to filter out non-text region and to remove false positives. The algorithm is robust to illumination, low resolution and uneven luminance from compressed videos. Effectiveness of PZM description leads to very few false positives comparing to other approached. Moreover resulting images can be used directly by OCR engines and no more processing is needed.

Face Recognition of Identical Twin is a challenging task due to the presence of a high degree of correlation in the overall appearance of the face. Few monozygotic twins help with business tricks such as fake insurance compensation. Most importantly, if one of the indistinguishable twins commits a serious crime, their unclear personalities cause confusion and uncertainty in court trials. The proposed method can be employed for these applications to overcome such harms. In this paper, The AdaBoost Technique is employed for the face detection using Haar features. This algorithm identifies the face region of the input image. The Pseudo Zernike Moment (PZM) and Difference of Gaussian (DoG) methods are utilized to extract the features from the face region detected by AdaBoost algorithm and stored in the databases in both training and testing phase. The Support Vector Machine (SVM) classifier distinguishes the twin's features by comparing both trained and tested features and identifies the culprit who is required as a result. The experimental results demonstrated the ability of the proposed method to recognize a pair of Identical twins.