Intelligent Digital Signage System Based on Gender Identification
01 Jan 2018-pp 251-262
TL;DR: This paper proposes an intelligent transformation of digital signage systems by making it more audience interactive by providing optimized information and appearance attractive multimedia content through the signage system.
Abstract: This paper proposes an intelligent transformation of digital signage systems by making it more audience interactive. The increase in flexibility and enhancement of digital signage display system can be done by providing optimized information and appearance attractive multimedia content through the signage system. This emphasizes more on the advertisement industry especially in public spaces like hotels, airports. The system has been designed to broadcast the advertisement on the signage display system based on the demographic features like gender of the observer. Real-time computer vision algorithms are applied to provide an observer-specific advertisement broadcast on the display system.
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TL;DR: The optimization model of signages in subway stations based on particle swarm optimization algorithm is proposed and the experimental results of Dongzhimen subway station show that the model has strong robustness in optimization, and the global best position can be found 100%.
Abstract: The imperfection of the guide signs in the subway will lead to many difficulties for passengers, which directly affects the operation efficiency of subway stations. In this paper, we use big data to analyze the problem of signages in Beijing subway, and propose the optimization model of signages in subway stations based on particle swarm optimization algorithm. The experimental results of Dongzhimen subway station in Beijing show that the model has strong robustness in optimization, and the global best position can be found 100%.
3 citations
Cites methods from "Intelligent Digital Signage System ..."
...In this study, real-time computer vision algorithm is applied to the electronic signage board, and different contents are displayed on the electronic signage board according to the observer’s gender and other demographic characteristics [19]....
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04 May 2022
TL;DR: This study presents an efficient real-time face gender detector (FGenCPU) that can run smoothly on a CPU implemented in digital signage to decide immediately and provide relevant and targeted ads while the audience faces it.
Abstract: Intelligent digital signage demands software to work lightly in real-time on embedded devices. Smart digital signage requires gender detection to deliver relevant and targeted ads automatically. This study presents an efficient real-time face gender detector (FGenCPU) that can run smoothly on a CPU implemented in digital signage to decide immediately and provide relevant and targeted ads while the audience faces it. The proposed architecture contains Multi-Perspective Convolution (MPConvNet) consisting of two main modules: backbone and recognition. An efficient extractor module with a multi-perspective contextual track through the various kernel sizes is utilized to apprehend different feature areas of the object. This architecture employs few filters in every convolution layer and generates few parameters. It makes the detector operate at high speed for real-time. The training is conducted on the UTKFace dataset to generate efficiently weighted models. The MPConvNet achieves competitive performance with other common and light architectures on UTKFace, Adience, and LFW datasets. Furthermore, the detector can run 38 frames per second when performing on a CPU in real-time.
3 citations
15 Apr 2019
TL;DR: Possibility of integrating possible information collection techniques for better prediction of viewers' interests are researched in the concept of the system that contains human detection, analytics, advertisement, and statistic modules.
Abstract: Digital signage has been gaining more and more popularity. However, personalization of digital signage is still challenging and it is getting even more difficult with the appearance of low regulating the ways of personal data storage and processing. Most advertisement frameworks can detect general information about viewers such as age and gender that is not enough to recognize his/her real interests. There are efforts aimed at detection of emotions and prediction interests based on the mood as well as usage of modern 3D cameras for collecting distance to the viewers and their heights. Also, the topic of detection logotypes in images is getting more popular. In this paper, we are researching possibilities of integrating possible information collection techniques for better prediction of viewers' interests. These are integrated into the concept of the system that contains human detection, analytics, advertisement, and statistic modules.
3 citations
01 Jan 2020
TL;DR: Euphony, a hand sanitation monitoring and indication system, that screens hand sanitation occasions and their quality, gives continuous input, helps the individual to remember intrigue when he/she is required to sanitize hands, and stores related information on a server for further use is presented.
Abstract: Hand sanitation acquiescence is immensely essential in hospitals, clinics and in the sphere of food industry. Caregiver’s docility with hand sanitation in the most adequate mechanism is mandate for restraining healthcare-associated infections (HAIs) in hospitals and clinics. Washing hands legitimately is the foundation of hand sanitation. Notwithstanding, the hand wash consistence rate by the labourers (parental figures, servers, gourmet experts, food processors, etc.) is not up to the mark. Observing hand wash consistence alongside an updated framework expands the consistence rate essentially. Quality of hand sanitation is also important, which can be accomplished by washing hands as per standard rules and guidelines. In this paper, we present Euphony, a hand sanitation monitoring and indication system, that screens hand sanitation occasions and their quality, gives continuous input, helps the individual to remember intrigue when he/she is required to sanitize hands, and stores related information on a server for further use. Euphony is vigorous, versatile and simple to introduce, and it conquers a large portion of the issues of existing related frameworks.
2 citations
TL;DR: In this article , a framework for an intelligent agent information service using digital human and deep learning technology is proposed, which can recognize the identity of individuals using facial features and provide personalized services through a digital human.
Abstract: This study proposes a framework for an intelligent agent information service using digital human and deep learning technology. The framework can recognize the identity of individuals using facial features and provide personalized services through a digital human. The personalized service is defined by a relevance graph based on personal data collected in advance. The proposed system can continuously evolve to recommend customized services using relevance graphs and dynamic data processing, gradually become more intelligent using additionally collected data. Moreover, it uses animation keyframe interpolation for natural and seamless digital human interaction and provides visual effects that are synchronized based on specific information collected for the intuitive service. The proposed system was tested on a school domain for two months, and a statistical domain feedback system based on a mathematical model that predicts service usage per unit time was developed using the recorded information. Additionally, we evaluate our system through user experience surveys.
2 citations
References
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TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Abstract: We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.
11,674 citations
TL;DR: An interactive, content-aware and cost-effective digital signage system using a monocular camera installed within the frame of a digital signage display to extract temporal, spatial and demographic features of the observers, which are further used for observer-specific broadcasting of digital signage content.
Abstract: In this paper we present the development of an interactive, content-aware and cost-effective digital signage system. Using a monocular camera installed within the frame of a digital signage display, we employ real-time computer vision algorithms to extract temporal, spatial and demographic features of the observers, which are further used for observer-specific broadcasting of digital signage content. The number of observers is obtained by the Viola and Jones face detection algorithm, whilst facial images are registered using multi-view Active Appearance Models. The distance of the observers from the system is estimated from the interpupillary distance of registered faces. Demographic features, including gender and age group, are determined using SVM classifiers to achieve individual observer-specific selection and adaption of the digital signage broadcasting content. The developed system was evaluated at the laboratory study level and in a field study performed for audience measurement research. Comparison of our monocular localization module with the Kinect stereo-system reveals a comparable level of accuracy. The facial characterization module is evaluated on the FERET database with 95% accuracy for gender classification and 92% for age group. Finally, the field study demonstrates the applicability of the developed system in real-life environments.
21 citations
Journal Article•
TL;DR: Experimental results on Indian face database show that HOG is more efficient approach for gender classification and improves gender recognition rate upto 95.56%.
Abstract: Gender Classification is the hot research topic from last two decades but still a gap exist between the requirements and actual performances. This gap lies due to the variation in pose, expression and illumination condition etc. Gender classification of face images is the process of identification of gender by their facial images. In this paper we compared the performance of two feature extraction algorithm i.e. Local binary pattern (LBP) and Histogram of oriented gradient (HOG) in order to determine the more efficient approach for gender classification from face images. Haar Cascade Classifier is used for the face detection from an image. Histogram equalization normalization technique is used for normalizing illumination effects. Support vector machine (SVM) is used as a classifier for gender classification. We implement gender classification system architecture using OpenCv 2.4.2. Indian face database (IFD) is used for the experiment . Experimental results on Indian face database show that HOG is more efficient approach for gender classification and improves gender recognition rate upto 95.56%. KeywordsGender classification, Haar cascade classifier, Histogram of oriented gradient, Local binary pattern, Support vector machine.
18 citations
TL;DR: Experimental results suggest that the proposed L-Fisherfaces provides a better representation and achieves higher accuracy in face recognition.
Abstract: An appearance-based face recognition approach called the L-Fisherfaces is proposed in this paper, By using Local Fisher Discriminant Embedding (LFDE), the face images are mapped into a face subspace for analysis. Different from Linear Discriminant Analysis (LDA), which effectively sees only the Euclidean structure of face space, LFDE finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. Different from Locality Preserving Projections (LPP) and Unsupervised Discriminant projections (UDP), which ignore the class label information, LFDE searches for the project axes on which the data points of different classes are far from each other while requiring data points of the same class to be close to each other. We compare the proposed L-Fisherfaces approach with PCA, LDA, LPP, and UDP on three different face databases. Experimental results suggest that the proposed L-Fisherfaces provides a better representation and achieves higher accuracy in face recognition.
17 citations
01 Jan 2014
TL;DR: The experimental results show the superior performance of the approach to the existing gender classifiers, which achieves excellent classification (100%) accuracy using approach (Continuous wavelet Transform and Random Forest) and compared with other classification Technique like Support Vector Machine, linear discriminate analysis and Fuzzy c – means.
Abstract: Gender classification such as classifying human face is not only challenging for computer, but even hard for human in some cases. This Paper use ORL database contain 400 images include both Male and Female Gender. Our experimental results show the superior performance of our approach to the existing gender classifiers. We achieves excellent classification (100%) accuracy using approach (Continuous wavelet Transform and Random Forest) and compared with other classification Technique like Support Vector Machine, linear discriminate analysis , k- nearest neighbor, Fuzzy c – means, Fuzzy c – means.
2 citations