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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Adhau, Janhavi Adsare, Vaibhav Jadhav, Deep Kshirsagar, Ketki Tamkhade, Jayashree Electronics and Telecommunications BRACT'S Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350308846

This idea presents a face recognition (FER) algorithm using a deep Siamese neural network (SNN) that stores local patterns of images in a similar space. We create networks to show similarity between pairs of ideas by comparing features with design. Additionally, we developed a new image matching (i.e., positive-negative matching) technique to introduce our Siamese model. Our Siamese model has a proven and accepted model for learning in an integrated environment. The validation method reduces the class variance by reducing the distance between features drawn from the same class, whereas a validation procedure raises the class variance by widening the gap between characteristics drawn from different classes. By using four layers of IoT we successfully implemented the multimodal authentication system with the Biometric and face recognition System as a mode for the authentication. In this project machine learning with IoT to make fulfill the product requirements and a more secure authentication system. © 2023 IEEE.

关键词： face recognition algorithm IoT Multimodal Authentication System Siamese Neural Network

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3D face recognition Using Anthropometric and Curvelet Features Fusion

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JOURNAL OF SENSORS 2016年第Pt.2期2016卷

作者： Song, Dan Luo, Jing Zi, Chunyuan Tian, Huixin Tianjin Polytech Univ Coll Elect Engn & Automat Tianjin 300387 Peoples R China Key Lab Adv Elect Engn & Energy Technol Tianjin 300387 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Sydney NSW 2522 Australia

Curvelet transform can describe the signal by multiple scales, and multiple directions. In order to improve the performance of 3D face recognition algorithm, we proposed an Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (Anthropometric Curvelet Fusion face recognition, ACFFR). First, the eyes, nose, and mouth feature regions are extracted by the Anthropometric characteristics and curvature features of the human face. Second, Curvelet energy features of the facial feature regions at different scales and different directions are extracted by Curvelet transform. At last, Euclidean distance is used as the similarity between template and objectives. To verify the performance, the proposed algorithm is compared with Anthroface3D and Curveletface3D on the Texas 3D FR database. The experimental results have shown that the proposed algorithm performs well, with equal error rate of 1.75% and accuracy of 97.0%. The algorithm we proposed in this paper has better robustness to expression and light changes than Anthroface3D and Curveletface3D.

关键词： Transform Wavelet Analysis Error analysis face recognition algorithm fusion (melting) FUSION Facial recognition Fusion, biological Three Dimensions algorithms Euclidean distance Facial Features Fusion reactions

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Visual System for Student Attendance Monitoring with Non-standard Situation Detection 12

Visual System for Student Attendance Monitoring with Non-sta...

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12th IEEE International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： Kainz, O. Cymbalak, D. Lamer, J. Jakab, F. Dept Comp & Informat Kosice Slovakia

ISBN: (纸本)9781479977406

In this paper we propose a visual system for monitoring of student attendance in seminars and lectures. Basic idea is to estimate the number of people in the room using face detection algorithms and subsequently utilize face recognition algorithms to determine the actual identification of persons ( students). Presented approach may be used for multiple purposes. Principal and primary purpose is to monitor attendance, which is possible thanks to university database. When implemented, system is expected to evaluate the attendance automatically or if necessary using collaborative authentication. Non-standard or anomaly detection is another feature that is to be provided by system, subject to tracking are hands, eyes and movement. Proposed solution is expected to improve and facilitate attendance monitoring of students at seminars and lectures. Further it may be used for anomaly prevention (e.g. cheating) and in specific cases for security or legal matters.

关键词： Visual System Students face recognition algorithm non-standard anomaly detection Lectures (teaching method) Persons Educational attendance

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Optimization of CRAZYTALK and SAI Application in Animation Platform Using face recognition and Motion Capture algorithms

International Core Journal of Engineering

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International Core Journal of Engineering 2024年第1期10卷 107-114页

作者： Kai Chen Minmin Han Shuhui Yang

In this study, the application of CRAZYTALK and SAI on animation platform was optimized by using face recognition and motion capture algorithms. Through the use of face recognition algorithm, it can realize the automatic capture and reproduction of facial expressions of cartoon characters, and improve the sense of reality of characters. At the same time, with the help of motion capture algorithm, the body movements of cartoon characters can be accurately captured and simulated, and the motion fluency of cartoon works can be improved. This research has positive significance for improving the user experience and content quality of animation platforms.

关键词： face recognition algorithm Motion Capture algorithm CRAZYTALK SAI Animation Platform Facial Expression Capture Body Movement Simulation User Experience Content Quality

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Directed Acyclic Graph Kernels for Action recognition

Directed Acyclic Graph Kernels for Action Recognition

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IEEE International Conference on Computer Vision (ICCV)

作者： Wang, Ling Sahbi, Hichem Telecom ParisTech CNRS LTCI Inst Mines Telecom F-75013 Paris France Telecom ParisTech CNRS LTCI F-75013 Paris France

ISBN: (纸本)9781479928392

One of the trends of action recognition consists in extracting and comparing mid-level features which encode visual and motion aspects of objects into scenes. However, when scenes contain high-level semantic actions with many interacting parts, these mid-level features are not sufficient to capture high level structures as well as high order causal relationships between moving objects resulting into a clear drop in performances. In this paper, we address this issue and we propose an alternative action recognition method based on a novel graph kernel. In the main contributions of this work, we first describe actions in videos using directed acyclic graphs (DAGs), that naturally encode pairwise interactions between moving object parts, and then we compare these DAGs by analyzing the spectrum of their sub-patterns that capture complex higher order interactions. This extraction and comparison process is computationally tractable, resulting from the acyclic property of DAGs, and it also defines a positive semi-definite kernel. When plugging the latter into support vector machines, we obtain an action recognition algorithm that overtakes related work, including graph-based methods, on a standard evaluation dataset.

关键词： recognition (Psychology) Seeds Directed Acyclic Graph Action Kernel image recognition algorithm face recognition algorithm Videotapes Support Vector Network coding

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A Transform Domain Modular Approach for Facial recognition Using Different Representations and Windowing Techniques 57

A Transform Domain Modular Approach for Facial Recognition U...

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57th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Chehata, Ramy C. G. Mikhael, Wasfy B. Atia, George Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781479941322

A face recognition algorithm based on a newly developed Transform Domain Modular (TDM) approach is proposed. In this approach, the spatial faces are divided into smaller sub-images, which are processed using non-overlapping and overlapping windows. Each image is subsequently transformed using a compressing transform such as the two dimensional discrete cosine transform. This produces the TDM2D and the TDM-Dia based on two-dimensional and diagonal representations of the data, respectively. The performance of this approach for facial image recognition is compared with the state of the art successful techniques. The test results, for noise free and noisy images, yield higher than 97.5% recognition accuracy. The improved recognition accuracy is achieved while retaining comparable or better computation complexity and storage savings.

关键词： face face recognition Principal component analysis Testing Time division multiplexing Training Transforms Facial recognition Transform time division multiplexing face face principal components analysis Functional training Image recognition window method state of the art face recognition algorithm multiprogramming Representation of data modular approach

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Illumination invariant object recognition

Illumination invariant object recognition

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International Conference on Image Processing

作者： Bichsel, M UNIV ZURICH MULTIMEDIA LABDEPT COMP SCICH-8057 ZURICHSWITZERLAND

ISBN: (纸本)0780331222

Varying illumination is severe problem for existing face recognition algorithms. Altering the light direction from left to right, for example, causes a change of contrast in large face regions and causes most face recognition algorithms to fail. Theoretical results, based on the law of incoherent light superposition, provide the solid ground on which a new illumination invariant recognition algorithm is derived. A face recognition experiment demonstrates that this algorithm indeed shows improved recognition performance even if the conditions, for which the theoretical results were derived, do not hold exactly.

关键词： face recognition algorithm Facial recognition Invariant object recognition incoherent light

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A Fast Eye Localization and Verification Method to Improve face Matching in Surveillance Videos

A Fast Eye Localization and Verification Method to Improve F...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lucas Sousa de Oliveira Dibio Leandro Borges Flavio de Barros Vidal Lin-Ching Chang Department of Computer Science University of Brasilia Department of Electronic Engineering and Computer Science The Catholic University of America

ISBN: (纸本)9781467317139

Automatic face recognition (FR) problem has been extensively studied and applied to domains including biometrics, security, authentication, surveillance, and identification. face recognition algorithms commonly use high dimensional information and are therefore computationally expensive. The use of wrongly detected features can confuse the recognition process and make it even slower. This paper presents a set of heuristic lightweight features to describe eye regions. These features are used to further classify detected eye regions into false positive and positive ones. Note that the detected eye regions are all very similar, making it hard to find a good set of features to separate them. The classification is done by simply applying a threshold proportional to the variance of the data. The method was able to correctly classify 49.8% of the false positive samples, which if applied to Viola and Jones best result could potentially turn its 93.7% performance into 96.8%. If the classification considers the pairs heuristics, the performance goes up to 83.3% at the expense of wrongly classifying positive samples. It would make the 93.7% become 98,9%.

关键词： False-positives Eye Viola Videotapes face recognition algorithm Heuristics

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Single-Sample face recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

Single-Sample Face Recognition with Image Corruption and Mis...

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IEEE Conference on Computer Vision and Pattern recognition

作者： Liansheng Zhuang Allen Y. Yang Zihan Zhou S. Shankar Sastry Yi Ma University of Science & Technology of China Department of EECS UC Berkeley Department of ECE University of Illinois

ISBN: (纸本)9781467364102

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. The SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images;and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

关键词： Facial recognition Misalignment SAFETY REVIEW COMMITTEE Lighting illumination images face recognition algorithm vandalism corruption sparse representation sparse representation-based classification

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A New Perspective - face recognition with Light-Field Camera

A New Perspective - Face Recognition with Light-Field Camera

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IAPR International Conference on Biometrics

作者： R. Raghavendra Bian Yang Kiran B. Raja Christoph Busch Norwegian Biometric Laboratory Gjovik University College

ISBN: (纸本)9781479903115

face recognition has received a substantial attention from industry and also academics. The improvement in the image sensors has further boosted the performance of the face recognition algorithm in real-world scenarios. In this paper, we evaluate the strength of the light-field camera for for face recognition applications. The main advantage of a light-field camera is that, it can provide different focus (or depth) images in a single capture which is not possible with a conventional 2D camera. We first collected a new face dataset using both the light-field and a conventional camera by simulating real-world scenarios. We then propose a new scheme to select the best focused face image from a set of focus images rendered by the light-field camera. Extensive experiments are carried out on our new face dataset to bring out the merits and demerits of employing the light-field camera for face recognition applications.

关键词： Facial recognition Cameras field camera face recognition algorithm Sensors (image) face face

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