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Due to different acquisition time, viewpoint, and sensor noise during the process of point cloud data acquisition, the captured point clouds typically exhibit partial overlapped and contain large amounts of noise and outliers. However, this circumstance tends to diminish the accuracy of point-to-point correspondence searches. Existing point-level methods rely on idealized point-to-point correspondences, which cannot be guaranteed in practical applications. To address above limitations, a noval network based on a geometry transformer and a Gaussian Mixture Model (GMM) is proposed, called GTGMM. Specifically, we formulate the registration problem as the problem of aligning the two Gaussian mixtures, leveraging the advantages of the statistic model and learned robust features to overcome the noise and outliers variants. We utilize a Local Feature Extractor (LFE) to extract structural features of point clouds, while the Transformer encoders establish global relations among the point clouds. Additionally, a geometry transformer network is introduced to capture geometric relations within the point cloud, and overlap scores are learned to reject non-overlapping regions. Utilizing overlap scores, point cloud features, and 3D point cloud coordinates, the matching parameters of GMM to calculate to guide the alignment of two point clouds. Experimental results on synthetic datasets and the real Terracotta Warriors data demonstrate that our method achieves high accuracy and robustness under various registration conditions. [ABSTRACT FROM AUTHOR]

Iris recognition is a secure and best-chosen biometric application in the digital world because of its unique characteristics. Day by day, the digital world plays a significant role in human life for various applications. The applications are vastly spread over secure applications of the nation such as border control applications, criminal investigations, postmortem studies, access the digital equipment, smart homes, smart appliances, smart cars etc. Due to the digitalization of the world, all the research communities, scientists, and industries are focusing on the biometric-based secured iris recognition system. Several researchers have done much work in this domain, but there is still a scope of improvement for various reasons, i.e., less speed and accuracy of the module. The researcher has implemented various algorithms based on traditional and neural network architectures. In this scenario, this paper gives a brief on different techniques and algorithms used by researchers to predict the age of human people using the iris. This paper discussed one hundred and one papers in the literature with various image segmentation, feature extraction and classification of the iris. This paper summarizes publicly available standard databases and various evaluation parameters, i.e., accuracy, precision, recall, f-score, etc. The research community evaluated the age prediction through the iris-based state-of-the-art algorithms with secure prediction, i.e., TPR, TNR, FPR, FNR. Finally, this paper provides the strengths and weaknesses of the various state of art algorithms, respectively, and summarizes the gaps in the existing technology with the scope of improvement. [ABSTRACT FROM AUTHOR]

Facial expression classification aims to recognize human emotion via face images. The major challenge of facial expression classification is how to extract discriminative features from the human face images to differentiate various emotions. To tackle this challenge, a new feature extraction approach is proposed in this paper. The proposed approach defines a new set of salient patterns at the facial keypoint locations. This is in contrast to conventional approaches that either represent the whole face image as a regular grid or use local image patches centered at all facial key point locations. Driven by the proposed salient patterns, both the geometric and textual features are extracted and then concatenated and further incorporated into a machine learning framework to perform facial expression classification. The proposed approach is evaluated in the well-known CK + benchmark dataset to demonstrate its superior performance. [ABSTRACT FROM AUTHOR]

The posterity challenging task in the Machine Intelligence field is to design a smarter system to identify the human emotions. Facial Emotion Recognition (FER) is a significant visual based tool to construct a smarter system that can recognize human emotions. The existing methods in FER are based on Action Units (AU), appearance and geometrical parameters. Nearly 7000 different combinations of AUs are used in AU to discriminate the emotions, which can be very expensive and increase processing time. Generalize appearance features across the universe is another challenging task. In this paper, a novel geometrical fuzzy based approach is presented to accurately recognize the emotions. The four corner features from eyes and mouth regions are extracted without considering reference face. The extracted features are used to define the quadrilateral shape that failed to match with the shapes in geometry. The degree of impreciseness exists in the quadrilateral is measured by the proposed Mixed Quadratic Shape Model (MQSM) using fuzzy membership functions. Finally, twelve fuzzy features are extracted from the membership functions and used by the classifier for validation. The CK, JAFFE and ISED datasets are used in the experiment to evaluate the performance of the MSQM. It is observed the proposed method performed better than the contemporary methods using twelve fuzzy features without reference image. [ABSTRACT FROM AUTHOR]

Geometric and textural features have been separately used in the literature for facial emotion classification. In this paper, we theoretically and empirically study the capture of facial expressions through geometric and texture-based features, and demonstrate that a simple concatenation of these features can lead to significant improvement in facial emotion classification. We also propose the use of the Directed Acyclic Graph SVM (DAGSVM) for facial emotion classification using the concatenated feature, by analyzing DAGSVM structures. We perform experiments using the well-known extended Cohn-Kanade (CK+), the MUG facial expression (MUG) and the Japanese Female Facial Expression (JAFFE) databases to evaluate the integration of geometric and textural features, and the use of DAGSVM for facial emotion classification. The said integration is found to be effective and DAGSVM is found to be computationally efficient in facial emotion classification. [ABSTRACT FROM AUTHOR]

Currently RNN-based methods achieve excellent performance on action recognition using skeletons. But the inputs of these approaches are limited to coordinates of joints, and they improve the performance mainly by extending RNN models in different ways and exploring relations of body parts directly from joint coordinates. Our method utilizes a universal spatial model perpendicular to the RNN model enhancement. Specifically, we propose two simple geometric features, inspired by previous work. With experiments on a 3-layer LSTM (Long Short-Term Memory) framework, we find that the geometric relational features based on vectors and normal vectors outperform other methods and achieve state-of-art results on two datasets. Moreover, we show that utilizing our features as input requires less data for training. [ABSTRACT FROM AUTHOR]

Automated analysis of cervical cancer images is considered as an attractive research in the biological fields. Due to the intensive advances in the digital technology and the light microscopy, the cellular imaging requires the continuous growing importance. Sophisticated methods are adopted to isolate the nuclei from the cytoplasm based on the boundary estimation and the analysis of intensity of blue cells to improve the abnormality prediction. The cell-based segmentation evolved in research studies assures the automatic assistance with an assumption of a single cell. Previous work [21] concentrates on the segmentation of abnormal region on single-cell images. This paper extends that work into the multi-cell images with the addition of geometrical features. The complex cell structure, poor contrast, and overlapping affect the cell segmentation performance. This paper enhances the performance of single-cell segmentation with the integrated feature vectors of geometrical (area, cell size, cell intensity and the maximum intensity) and Gray level Co-occurrence Matrix (GLCM) to improve the abnormality level prediction.. Initially, the Neighborhood Concentric Filtering (NCF) is applied on the input slides to remove the noise present in the image and enhance the intensity level. Then, the initial level cluster formation and masking are performed on the noise-free image. The Optimal Weight Updating with the Multi-Level set (OWU-ML) estimates the Region of Interest (ROI) and segments the blue cell and cytoplasm. The clear analysis of blue cell indicates the exact classification of abnormal levels in the images. The combination of geometrical and GLCM extracts the texture pattern features of the blue cell, cytoplasm and the nucleus portions in the form of angle variations. Finally, the Neural Network-based RVM classifier predicts the classes of (normal and abnormal) cervical images. The integration of novel methods such as OWU-ML segmentation, GLCM + geometrical feature extraction and NN-RVM classification improves the abnormal prediction performance and assures the suitability in multi-cell cervical image handling in biological applications. [ABSTRACT FROM AUTHOR]

Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets. [ABSTRACT FROM AUTHOR]

Facial expressions convey nonverbal cues which play an important role in interpersonal relations, and are widely used in behavior interpretation of emotions, cognitive science, and social interactions. In this paper we analyze different ways of representing geometric feature and present a fully automatic facial expression recognition (FER) system using salient geometric features. In geometric feature-based FER approach, the first important step is to initialize and track dense set of facial points as the expression evolves over time in consecutive frames. In the proposed system, facial points are initialized using elastic bunch graph matching (EBGM) algorithm and tracking is performed using Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point, line and triangle composed of tracking results of facial points. The most discriminative line and triangle features are extracted using feature selective multi-class AdaBoost with the help of extreme learning machine (ELM) classification. Finally the geometric features for FER are extracted from the boosted line, and triangles composed of facial points. The recognition accuracy using features from point, line and triangle are analyzed independently. The performance of the proposed FER system is evaluated on three different data sets: namely CK+, MMI and MUG facial expression data sets. [ABSTRACT FROM AUTHOR]