Drive in Peace

In this paper, in order to implement a computer vision-based recognition system of driving fatigue. In addition to detecting human face in different light sources and the background conditions, and tracking eyes state combined with fuzzy logic to determine whether the driver of the physiological phenomenon of fatigue from face of detection. Driving fatigue recognition has been valued highly in recent years by many scholars and used extensively in various fields, for example, driver activity tracking, driver visual attention monitoring, and in-car camera systems.In this paper, we use the Windows operating system as the development environment, and utilize PC as the hardware platform. First, the system uses a camera to obtain the frame with a human face to detect, and then uses the frame to set the appropriate skin color scope to find face. Next, we find and mark out the eyes and the lips from the selected face area. Finally, we combine the image processing of eyes features with fuzzy logic to determine the driver's fatigue level, and make the graphical man-machine interface with MiniGUI for users to operate.Along with that we are using Arduino Uno microcontroller which is connected to MQ2-smoke sensor through which we can detect smoke which appears through issue in the car system. The results of experiment show that we achieve this system on PC platform successfully

I know you are beautiful even without looking at you: discrimination of facial beauty in peripheral vision

Prior research suggests that facial attractiveness may capture attention at parafovea. However, little is known about how well facial beauty can be detected at parafoveal and peripheral vision. Participants in this study judged relative attractiveness of a face pair presented simultaneously at several eccentricities from the central fixation. The results show that beauty is not only detectable at parafovea but also at periphery. The discrimination performance at parafovea was indistinguishable from the performance around the fovea. Moreover, performance was well above chance even at the periphery. The results show that the visual system is able to use the low spatial frequency information to appraise attractiveness. These findings not only provide an explanation for why a beautiful face could capture attention when central vision is already engaged elsewhere, but also reveal the potential means by which a crowd of faces is quickly scanned for attractiveness

Deep Face Morph Detection Based on Wavelet Decomposition

Morphed face images are maliciously used by criminals to circumvent the official process for receiving a passport where a look-alike accomplice embarks on requesting a passport. Morphed images are either synthesized by alpha-blending or generative networks such as Generative Adversarial Networks (GAN). Detecting morphed images is one of the fundamental problems associated with border control scenarios. Deep Neural Networks (DNN) have emerged as a promising solution for a myriad of applications such as face recognition, face verification, fake image detection, and so forth. The Biometrics communities have leveraged DNN to tackle fundamental problems such as morphed face detection. In this dissertation, we delve into data-driven morph detection which is of great significance in terms of national security. We propose several wavelet-based face morph detection schemes which employ some of the computer vision algorithms such as image wavelet analysis, group sparsity, feature selection, and the visual attention mechanisms. Wavelet decomposition enables us to leverage the fine-grained frequency content of an image to boost localizing manipulated areas in an image. Our methodologies are as follows: (1) entropy-based single morph detection, (2) entropy-based differential morph detection, (3) morph detection using group sparsity, and (4) Attention aware morph detection. In the first methodology, we harness mismatches between the entropy distribution of wavelet subbands corresponding to a pair of real and morph images to find a subset of most discriminative wavelet subbands which leads to an increase of morph detection accuracy. As the second methodology, we adopt entropy-based subband selection to tackle differential morph detection. In the third methodology, group sparsity is leveraged for subband selection. In other words, adding a group sparsity constraint to the loss function of our DNN leads to an implicit subband selection. Our fourth methodology consists of different types of visual attention mechanisms such as convolutional block attention modules and self-attention resulting in boosting morph detection accuracy. We demonstrate efficiency of our proposed algorithms through several morph datasets via extensive evaluations as well as visualization methodologies

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Using facial recognition services as implicit feedback for recommenders

User authentication and feedback gathering are crucial aspects for recommender systems. The most common implementations, a username / password login and star rating systems, require user interaction and a cognitive effort from the user. As a result, users opt to save their password in the interface and optional feedback with a star rating system is often skipped, especially for applica- tions such as video watching in a home environment. In this article, we propose an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics. Using facial recognition with a camera in a tablet, smartphone, or smart TV, the persons in front of the screen can be identified in order to link video watch- ing sessions to their user profile. During video watching, implicit feedback is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An emotion finger- print, which is defined as a unique spectrum of expected emotions for a video scene, is compared to the recognized emotions in order to estimate the experience of a user while watching. An evaluation with a test panel showed that happiness can be most accurately detected and the recognized emotions are correlated with the user’s star rating

Faces do not capture special attention in children with autism spectrum disorder: a change blindness study

Two experiments investigated attention of children with autism spectrum disorder (ASD) to faces and objects. In both experiments, children (7- to 15-year-olds) detected the difference between 2 visual scenes. Results in Experiment 1 revealed that typically developing children (n = 16) detected the change in faces faster than in objects, whereas children with ASD (n = 16) were equally fast in detecting changes in faces and objects. These results were replicated in Experiment 2 (n = 16 in children with ASD and 22 in typically developing children), which does not require face recognition skill. Results suggest that children with ASD lack an attentional bias toward others' faces, which could contribute to their atypical social orienting

Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools

While detecting and interpreting temporal patterns of non–verbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human–centered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement