scholarly journals Perceiving animacy in own- and other-species faces

2018 ◽  
Author(s):  
Benjamin Balas ◽  
Amanda Auen
Keyword(s):  
Face Recognition ◽  
Common Mechanism ◽  
Differential Impact ◽  
Visual World ◽  
Visual Expertise ◽  
Face Images ◽  
Open Question ◽  
Human Faces ◽  
The Impact ◽  
Species Category

Though artificial faces of various kinds are rapidly becoming more and more life-like due to advances in graphics technology (Suwajanakorn et al., 2015; Booth et al., 2017), observers can typically distinguish real faces from artificial faces. In general, face recognition is tuned to experience such that expert-level processing is most evident for faces that we encounter frequently in our visual world, but the extent to which face animacy perception is also tuned to in-group vs. out-group categories remains an open question. In the current study, we chose to examine how the perception of animacy in human faces and dog faces was affected by face inversion and the duration of face images presented to adult observers. We hypothesized that the impact of these manipulations may differ as a function of species category, indicating that face animacy perception is tuned for in-group faces. Briefly, we found evidence of such a differential impact, suggesting either that distinct mechanisms are used to evaluate the “life” in a face for in-group and out-group faces, or that the efficiency of a common mechanism varies substantially as a function of visual expertise.

scholarly journals Template Based Pose and Illumination Invariant Face Recognition

2020 ◽  
Vol 8 (5) ◽  
pp. 3220-3229
Keyword(s):  
Face Recognition ◽  
Gabor Wavelet ◽  
Mathematical Methods ◽  
Query Image ◽  
Illumination Invariant ◽  
Face Images ◽  
The Face ◽  
Similar Images ◽  
Proper Face ◽  
The Impact

This article presents a method “Template based pose and illumination invariant face recognition”. We know that pose and Illumination are important variants where we cannot find proper face images for a given query image. As per the literature, previous methods are also not accurately calculating the pose and Illumination variants of a person face image. So we concentrated on pose and Illumination. Our System firstly calculates the face inclination or the pose of the head of a person with various mathematical methods. Then Our System removes the Illumination from the image using a Gabor phase based illumination invariant extraction strategy. In this strategy, the system normalizes changing light on face images, which can decrease the impact of fluctuating Illumination somewhat. Furthermore, a lot of 2D genuine Gabor wavelet with various orientations is utilized for image change, and numerous Gabor coefficients are consolidated into one entire in thinking about spectrum and phase. Finally, the light invariant is acquired by separating the phase feature from the consolidated coefficients. Then after that, the obtained Pose and illumination invariant images are convolved with Gabor filters to obtain Gabor images. Then templates will be extracted from these Gabor images and one template average is generated. Then similarity measure will be performed between query image template average and database images template averages. Finally the most similar images will be displayed to the user. Exploratory results on PubFig database, Yale B and CMU PIE face databases show that our technique got a critical improvement over other related strategies for face recognition under enormous pose and light variation conditions.

Real World Applications

2019 ◽  
pp. 149-164
Author(s):  
Massimo Tistarelli ◽  
Stan Z. Li
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
Access Control ◽  
Real World ◽  
Face Images ◽  
Real World Applications ◽  
The Subject ◽  
Human Faces ◽  
Machine Interaction ◽  
And Robotics

The analysis of face images has been extensively applied for the recognition of individuals in several application domains. Most notably, faces not only convey information about the identity of the subject, but also a number of ancillary information, which may be equally useful to anonymously determine the characteristics of an individual. Even though the first applications of face recognition have been related to security and access control, nowadays the analysis of human faces is related to several applications including law enforcement, man-machine interaction, and robotics, just to mention a few. This chapter explores the analysis of face images.

scholarly journals UNSUPERVISED DISCOVERY OF VISUAL FACE CATEGORIES

2013 ◽  
Vol 22 (01) ◽  
pp. 1250029 ◽  
Author(s):  
SHICAI YANG ◽  
GEORGE BEBIS ◽  
MUHAMMAD HUSSAIN ◽  
GHULAM MUHAMMAD ◽  
ANWAR M. MIRZA
Keyword(s):  
Face Recognition ◽  
Visual Cues ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Local Features ◽  
High Accuracy ◽  
Face Images ◽  
Face Categorization ◽  
Human Faces ◽  
Category Discovery

Human faces can be arranged into different face categories using information from common visual cues such as gender, ethnicity, and age. It has been demonstrated that using face categorization as a precursor step to face recognition improves recognition rates and leads to more graceful errors. Although face categorization using common visual cues yields meaningful face categories, developing accurate and robust gender, ethnicity, and age categorizers is a challenging issue. Moreover, it limits the overall number of possible face categories and, in practice, yields unbalanced face categories which can compromise recognition performance. This paper investigates ways to automatically discover a categorization of human faces from a collection of unlabeled face images without relying on predefined visual cues. Specifically, given a set of face images from a group of known individuals (i.e., gallery set), our goal is finding ways to robustly partition the gallery set (i.e., face categories). The objective is being able to assign novel images of the same individuals (i.e., query set) to the correct face category with high accuracy and robustness. To address the issue of face category discovery, we represent faces using local features and apply unsupervised learning (i.e., clustering). To categorize faces in novel images, we employ nearest-neighbor algorithms or learn the separating boundaries between face categories using supervised learning (i.e., classification). To improve face categorization robustness, we allow face categories to share local features as well as to overlap. We demonstrate the performance of the proposed approach through extensive experiments and comparisons using the FERET database.

Detection Algorithm Research of Face Pose Based on Stereoscopic Vision and Biological Characteristics

2012 ◽  
Vol 460 ◽  
pp. 30-34
Author(s):  
Peng Xu ◽  
Yuan Men Zhou
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Stereoscopic Vision ◽  
Accuracy Rate ◽  
Face Images ◽  
Face Features ◽  
Key Points ◽  
Left And Right ◽  
The Impact

The paper introduces a kind of detection method of face pose based on stereoscopic vision technology, approximately divides head’s deflexion into three plane rotations. By calculating the deflexion angle of three directions, you can determine the face’s pose. This method obtains face images by the left and right video channels, first analyses the similarity of double channels’ images to obtain three-dimensional information of face features’ key points. Then calculates three deflexion angles according to these information, therefore can correspondingly adjust and deform the original image to get standard frontal face image, and provides correction image for the latter face recognition. By this method the impact of pose change to face recognition can be reduced obviously in the earlier stage, so the system’s overall recognition accuracy rate is enhanced effectively.

scholarly journals A smart login system using face detection and recognition by ORB algorithm

2020 ◽  
Vol 20 (2) ◽  
pp. 1078
Author(s):  
Mohammad Jahangir Alam ◽  
Tanjia Chowdhury ◽  
Md. Shahzahan Ali
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Punch Card ◽  
Effective Technique ◽  
Face Images ◽  
Different Types ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Human Faces

<p>We can identify human faces using a web Camera which is known as Face Detection.  This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image.  This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of the computer relating the information into a database. When any body tries to enter into any room or premises through this login system, the system captures the image of that particular person and matches the image with the stored image. If this image matches with the stored image then the system allows the person to enter the room or premises, otherwise the system denies entry. This face recognition login system is very effective, reliable and secured. This research has used the Viola and Jones algorithm for face detection and ORB for image matching in face recognition and Java, MySql, OpenCV, and iReport are used for implementation.</p>

Real World Applications

2014 ◽  
pp. 1-15
Author(s):  
Massimo Tistarelli ◽  
Stan Z. Li
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
Access Control ◽  
Real World ◽  
Face Images ◽  
Real World Applications ◽  
The Subject ◽  
Human Faces ◽  
Machine Interaction ◽  
And Robotics

The analysis of face images has been extensively applied for the recognition of individuals in several application domains. Most notably, faces not only convey information about the identity of the subject, but also a number of ancillary information, which may be equally useful to anonymously determine the characteristics of an individual. Even though the first applications of face recognition have been related to security and access control, nowadays the analysis of human faces is related to several applications including law enforcement, man-machine interaction, and robotics, just to mention a few. This chapter explores the analysis of face images.

scholarly journals Smart Door Lock/Unlock Using Raspberry Pi

2020 ◽  
pp. 543-548
Author(s):  
Sandesh R ◽  
Avinash Sridhar ◽  
Rishikesh T P ◽  
Saniya Farheen ◽  
Sara Tameem
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Images ◽  
Face Features ◽  
Image Capturing ◽  
The Face ◽  
Face Detection And Recognition ◽  
Door Lock ◽  
Human Faces

This paper deals with the proposed system for smart and savvy door lock recognition system which is essentially for identification of human faces and mainly for home security. This is divided into two sub systems. First is image capturing, then comes face detection and recognition and finally automatic door access management. Open CV is mainly used for Face Recognition because it uses Eigen faces which compares the face images and produces it without losing vital face features, facial images of various persons are going to be stored in database. The purpose of the paper is to take face recognition to height which can replace the use of standard passwords, pins and patterns, adding more security to our life. The process carried out by raspberry pi is fast and makes the system work smoother.

Face Recognition Based on Template Matching and Skin-Color Segmentation

2011 ◽  
Vol 271-273 ◽  
pp. 165-170 ◽  
Author(s):  
Zhi Wen Wang ◽  
Shao Zi Li
Keyword(s):  
Face Recognition ◽  
Skin Color ◽  
Template Matching ◽  
Recognition Rate ◽  
Color Space ◽  
Facial Feature ◽  
Gaussian Mixture ◽  
Color Segmentation ◽  
Human Faces ◽  
The Impact

In order to overcome these deficiencies that computation of recognition algorithm based on template matching is very high and the recognition rate of recognition algorithms based on skin-color segmentation is low, and is vulnerable to the impact of background which is similar with skin-color, face recognition algrithom based on skin color segmentation and template matching is presented in this paper. According to the clustering properties that the skin-color of human faces have emerged in the YCbCr color space, the regions closing to facial skin color are separated from the image by using Gaussian mixture model in order to achieve the purpose of rapidly detecting the external face of human face. Adaptive template matching is used to overcome the affect of the backgrounds which are similar with skin color on face recognition. Computation in the matching process is reduced by using the second matching algorithm. Extraction of face images by using singular value features is used to identify faces and to reduce the dimensions of the eigenvalue matrix in the course of facial feature extraction. Experimental results show that proposed method can rapidly recongnise human faces, and improve the accuracy of face recognition.

Recognizing Face Images with Disguise Variations

2014 ◽  
pp. 227-251
Author(s):  
Neslihan Kose ◽  
Jean-Luc Dugelay ◽  
Richa Singh ◽  
Mayank Vatsa
Keyword(s):  
Face Recognition ◽  
Image Quality ◽  
State Of The Art ◽  
Face Images ◽  
Important Challenge ◽  
The Impact ◽  
Research Studies

Challenges in automatic face recognition can be classified in several categories such as illumination, image quality, expression, pose, aging, and disguise. In this chapter, the authors focus on recognizing face images with disguise variations. Even though face recognition with disguise variations is a major challenge, the research studies on this topic are limited. In this study, first disguise variations are defined followed by an overview of the existing databases used for disguise analysis. Next, the studies that are dedicated to the impact of disguise variations on existing face recognition techniques are introduced. Finally, a collection of several techniques proposed in state-of-the-art which are robust against disguise variations is provided. This study shows that disguise variations have a significant impact on face recognition; hence, more robust approaches are required to address this important challenge.

scholarly journals Robot Face Memorability is Affected by Uncanny Appearance

2021 ◽  
Author(s):  
Allie Geiger ◽  
Benjamin Balas
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Memory Task ◽  
Human Robot Interaction ◽  
Study Phase ◽  
Human Face ◽  
Human Face Recognition ◽  
Social Agents ◽  
Face Images ◽  
Human Faces

Human face recognition is influenced by various social and environmental constructs. For example, both age and race can affect the likelihood that a human face will be correctly recalled. Interestingly, general face appearance (i.e. friendly or untrustworthy faces) can also influence memorability. As human-robot interaction (HRI) becomes more commonplace, understanding what factors influence face recognition for non-human social agents is increasingly important. In particular, while there is a growing literature comparing the recognition of real human faces to computer-generated face images, comparisons between human face processing and robot face processing are largely unexplored. Here, we examined how the uncanny/eeriness of robot-faces affects memorability by using a 2AFC old/new task with various robot faces. Participants rated robot and human faces on perceived uncanniness during a study phase and were subsequently given a surprise memory task with only a fraction of the previously-encountered robot faces. Our results suggest that robots who are rated as more uncanny are more memorable than those that do not elicit the eerie feelings that correspond with uncanny faces: The more uncanny the robot face, the more accurately and quickly they were recalled. We discuss these results in the context of the design of social agents for HRI and also vis-a-vis theories of human face recognition and memory.

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