identification by facial geometry
8618902
Facial Geometry Identification through Fuzzy Patterns with RGBD Sensor
Automatic human facial recognition is an important and complicated task; it is necessary to design algorithms capable of recognizing the constant patterns in the face and to use computing resources efficiently. In this paper we present a novel algorithm to recognize the human face in real time; the system's input is the depth and color data from the Microsoft KinectTM device. The algorithm recognizes patterns/shapes on the point cloud topography. The template of the face is based in facial geometry; the forensic theory classifies the human face with respect to constant patterns: cephalometric points, lines, and areas of the face. The topography, relative position, and symmetry are directly related to the craniometric points. The similarity between a point cloud cluster and a pattern description is measured by a fuzzy pattern theory algorithm. The face identification is composed by two phases: the first phase calculates the face pattern hypothesis of the facial points, configures each point shape, the related location in the areas, and lines of the face. Then, in the second phase, the algorithm performs a search on these face point configurations.
Computacion y Sistemas
Victor Fernandez-Cervantes Arturo Garcia Marco Antonio Ramos Andres Mendez
181618
Perception of global facial geometry is modulated through experience
Identification of personally familiar faces is highly efficient across various viewing conditions. While the presence of robust facial representations stored in memory is considered to aid this process, the mechanisms underlying invariant identification remain unclear. Two experiments tested the hypothesis that facial representations stored in memory are associated with differential perceptual processing of the overall facial geometry. Subjects who were personally familiar or unfamiliar with the identities presented discriminated between stimuli whose overall facial geometry had been manipulated to maintain or alter the original facial configuration (see Barton, Zhao Keenan, 2003) The results demonstrate that familiarity gives rise to more efficient processing of global facial geometry, and are interpreted in terms of increased holistic processing of facial information that is maintained across viewing distances.
PeerJ
Meike Ramon
125362475
An Automatic Facial Localization Tracking Identification Biometric System through PCA and Wavelet Distribution in 3D Mesh Environment
Background/Objectives: In this paper, the process of automatically facial region localization and tracking in video frames through 3D mesh model is estimated. Methods/Statistical Analysis: The morphological regions of the face are modeled into 18 geometry based regions based on the various shape and expressions. The feature is estimated based on the covariance matrix in high region space. Then, it undergoes to PCA to estimate facial deformations. Findings: The patterns feature is extracted and it mapped with the multi geometry mapping. Then, the corresponding wavelet transforms extracted region into various dimension for geometry matching for classification. The proposed method achieves robustness based on its accurate transformation through wavelet analysis. The XM2VTSDB multi modal face database is used to compare the real video sequence images. Application/Improvements: The experimental evaluation shows favorable results and yields 99% detection rate over 15000 video frame images. The frames in the facial tracking data are calculated through various parameters and compared with the ground truth against standard Euler angles of the muti modal database.
B Rashida Munir Rabbani
211120198
Gender Recognition from Facial Images using Convolutional Neural Network
Gender is one of the facial attributes whose identification serves crucial step in systems including smart billboards, emotion recognition and surveillance systems. This paper explores a deep learning based solution for automatic detection of gender from face images of a well balanced dataset. The solution involves a transfer learning framework where the knowledge is reused out of a deep learning model which performed quite well in classification task of other domain. We investigate the reusability of Visual Geometry Group 16 (VGG16) an off the shelf Convolutional Neural Network (CNN) model which is pre trained upon large dataset of natural images. Fine tuning a segment of the CNN upon the moderately sized dataset yields better performance than obtained from the state of the art approaches when implemented upon the public LFW Gender dataset.
2019 Fifth International Conference on Image Information Processing (ICIIP)
Shubham Mittal Shiva Mittal
115523583
3D Facial Action Units and Expression Recognition using a Crisp Logic
This work proposes a method for recognizing the main 13 Facial Action Units and the 6 basic emotions. The methodologies rely on Differential Geometry to extract relevant discriminant features from the query faces, and on some linear quantities used as measures: Euclidean, geodesic, and angles between 17 automatically extracted soft tissue landmarks. A thresholding system which evaluates local properties of connected regions, selected through tailored geometrical descriptors, supports the identification of the AUs. Then, a technique based on crisp logic allows the identification of the global expression. The three dimensional context has been preferred due to its invariance to different lightening/make up/camouflage conditions.
Stefano Tornincasa Enrico Vezzetti Sandro Moos Maria Grazia Violante Federica Marcolin Nicole Dagnes Luca Ulrich and Giorgio Fantini Tregnaghi
2387858
Irrelevant visual faces influence haptic identification of facial expressions of emotion
This study demonstrates that when people attempt to identify a facial expression of emotion (FEE) by haptically exploring a 3D facemask, they are affected by viewing a simultaneous, task irrelevant visual FEE portrayed by another person. In comparison to a control condition, where visual noise was presented, the visual FEE facilitated haptic identification when congruent (visual and haptic FEEs same category) When the visual and haptic FEEs were incongruent, haptic identification was impaired, and error responses shifted toward the visually depicted emotion. In contrast, visual emotion labels that matched or mismatched the haptic FEE category produced no such effects. The findings indicate that vision and touch interact in FEE recognition at a level where featural invariants of the emotional category (cf. precise facial geometry or general concepts) are processed, even when the visual and haptic FEEs are not attributable to a common source. Processing mechanisms behind these effects are considered.
Attention, perception psychophysics
Roberta L Klatzky Aneta Abramowicz Cheryl L Hamilton Susan J Lederman
67889170
3D Face Expression Recognition via Geometry A Preparatory Path
This work presents a preliminary automatic 3D facial expression recognition procedure based on point by point mapping of seventeen Differential Geometry descriptors onto the query facial depth map, which is then partitioned into seventy nine regions. Then, mean, median, mode, volumes, histograms are computed for each region and for each descriptor, and adopted as features, to reach a diverse set of parameters representing the shape description of the query face. Each parameter set, given by a geometrical descriptor, a region, and a feature, form a trio, whose featuring numerical values are compared with appropriate thresholds, set via experimentation. This allows the identification of the emotion based expression of the probe 3D face among the six basic ones (anger, disgust, fear, joy, sadness, surprise) The algorithm has been tested on the Bosphorus database and is conceived to be applied to security, marketing, medical contexts. The three dimensional context has been chosen due to its invariance to different lightening/make up/camouflage conditions.
Enrico Vezzetti Federica Marcolin Stefano Tornincasa F Baldassarre D Buisan Vicente
117563466
Computational Conformal Geometry
This new volume presents thorough introductions to the theoretical foundations as well as to the practical algorithms of computational conformal geometry. These have direct applications to engineering and digital geometric processing, including surface parameterization, surface matching, brain mapping, 3 D face recognition and identification, facial expression and animation, dynamic face tracking, mesh spline conversion, and more. Computational conformal geometry is an emerging inter disciplinary field, with applications to algebraic topology, differential geometry and Riemann surface theories applied to geometric modeling, computer graphics, computer vision, medical imaging, visualization, scientific computation, and many other engineering fields.
Shing-Tung Yau Xianfeng Gu
67114221
Decision Based Spectral Embedding Approach for Identifying Facial Behaviour on RGB D Images
Automatic identification of various facial movements and expressions with high recognition value is important for human computer interaction as the facial behaviour of a human can be treated as an important factor for information representation as well as communication. A high deviation of human appearance and existence of noisy contextual background makes the human pose analysis is hard to achieve. A number of basic factors such as cluttered background, occlusion, and camera movement and illumination variations degrade the image quality resulting in poor performance for identifying different facial expressions. Moreover the identification of the automatic feature detection in facial behaviour requires high degree of correlation between the training and test images. Our proposed work tries to address the mentioned problems and resolve to some extent. In this methodology, a Decision based Spectral Embedding approach combining appearance and geometry based features for head pose estimation and facial expression recognition by minimizing the objective function which leads to selection of optimal set of fiducial points. The method preserves the local information from different facial views for mapping neighbouring input to its corresponding output, resulting in low dimensional representation for encoding the relationships of the data. The proposed methodology is validated with the RGB D data set and real depth images and compared with the state of art methods for analyzing the performance of recognition of facial behavior.
Deepak Kumar Jain Raj Kumar Neha Jain
3502966
Non manual grammatical marker recognition based on multi scale, spatio temporal analysis of head pose and facial expressions
Abstract Changes in eyebrow configuration, in conjunction with other facial expressions and head gestures, are used to signal essential grammatical information in signed languages. This paper proposes an automatic recognition system for non manual grammatical markers in American Sign Language (ASL) based on a multi scale, spatio temporal analysis of head pose and facial expressions. The analysis takes account of gestural components of these markers, such as raised or lowered eyebrows and different types of periodic head movements. To advance the state of the art in non manual grammatical marker recognition, we propose a novel multi scale learning approach that exploits spatio temporally low level and high level facial features. Low level features are based on information about facial geometry and appearance, as well as head pose, and are obtained through accurate 3D deformable model based face tracking. High level features are based on the identification of gestural events, of varying duration, that constitute the components of linguistic non manual markers. Specifically, we recognize events such as raised and lowered eyebrows, head nods, and head shakes. We also partition these events into temporal phases. We separate the anticipatory transitional movement (the onset from the linguistically significant portion of the event, and we further separate the core of the event from the transitional movement that occurs as the articulators return to the neutral position towards the end of the event (the offset This partitioning is essential for the temporally accurate localization of the grammatical markers, which could not be achieved at this level of precision with previous computer vision methods. In addition, we analyze and use the motion patterns of these non manual events. Those patterns, together with the information about the type of event and its temporal phases, are defined as the high level features. Using this multi scale, spatio temporal combination of low and high level features, we employ learning methods for accurate recognition of non manual grammatical markers in ASL sentences.
Image Vis. Comput.
Jingjing Liu Bo Liu Shaoting Zhang Fei Yang Peng Yang Dimitris N Metaxas C Neidle