scholarly journals Distillation of an End-to-End Oracle for Face Verification and Recognition Sensors

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1369
Author(s):  
Francesco Guzzi ◽  
Luca De Bortoli ◽  
Romina Soledad Molina ◽  
Stefano Marsi ◽  
Sergio Carrato ◽  
...  
Keyword(s):  
Face Recognition ◽  
Complex Model ◽  
Face Verification ◽  
Security Systems ◽  
Alignment Procedure ◽  
Open Set ◽  
Minimal Structure ◽  
Sensor Module ◽  
Network Methods ◽  
End To End

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.

Joint Bayesian guided metric learning for end-to-end face verification

2018 ◽  
Vol 275 ◽  
pp. 560-567 ◽  
Author(s):  
Di Chen ◽  
Chunyan Xu ◽  
Jian Yang ◽  
Jianjun Qian ◽  
Yuhui Zheng ◽  
...  
Keyword(s):  
Metric Learning ◽  
Face Verification ◽  
End To End

scholarly journals Automatic Attendance Management System under Unconstrained Video using Face Recognition

2020 ◽  
Vol 9 (10) ◽  
pp. 229-232
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Management System ◽  
Traditional Method ◽  
Surveillance Camera ◽  
Recognition Systems ◽  
End To End ◽  
Learning By Using

Attendance Management System under unconstrained video using face recognition technology has made a great variation from the traditional method of attendance marking system. This attendance management system has been developed under the domain of Deep Learning by using Face recognition. Automatic Attendance Management under unconstrained video using face recognition systems which automatically mark attendance by detecting end to end face from the frames obtained from live stream video of surveillance camera which placed in center of the classroom. From the recognized faces, it will be compared with stored images in database, then the attendance report will be generated and it also provides attendance reports to parents of the absentee’s student.

scholarly journals Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1046 ◽  
Author(s):  
Abeer D. Algarni ◽  
Ghada M. El Banby ◽  
Naglaa F. Soliman ◽  
Fathi E. Abd El-Samie ◽  
Abdullah M. Iliyasu
Keyword(s):  
Face Recognition ◽  
Characteristic Curve ◽  
Similarity Index ◽  
Random Projection ◽  
Personal Identification ◽  
Random Projections ◽  
Person Identification ◽  
Security Systems ◽  
Biometric Data ◽  
Rich Diversity

To circumvent problems associated with dependence on traditional security systems on passwords, Personal Identification Numbers (PINs) and tokens, modern security systems adopt biometric traits that are inimitable to each individual for identification and verification. This study presents two different frameworks for secure person identification using cancellable face recognition (CFR) schemes. Exploiting its ability to guarantee irrevocability and rich diversity, both frameworks utilise Random Projection (RP) to encrypt the biometric traits. In the first framework, a hybrid structure combining Intuitionistic Fuzzy Logic (IFL) with RP is used to accomplish full distortion and encryption of the original biometric traits to be saved in the database, which helps to prevent unauthorised access of the biometric data. The framework involves transformation of spatial-domain greyscale pixel information to a fuzzy domain where the original biometric images are disfigured and further distorted via random projections that generate the final cancellable traits. In the second framework, cancellable biometric traits are similarly generated via homomorphic transforms that use random projections to encrypt the reflectance components of the biometric traits. Here, the use of reflectance properties is motivated by its ability to retain most image details, while the guarantee of the non-invertibility of the cancellable biometric traits supports the rationale behind our utilisation of another RP stage in both frameworks, since independent outcomes of both the IFL stage and the reflectance component of the homomorphic transform are not enough to recover the original biometric trait. Our CFR schemes are validated on different datasets that exhibit properties expected in actual application settings such as varying backgrounds, lightings, and motion. Outcomes in terms standard metrics, including structural similarity index metric (SSIM) and area under the receiver operating characteristic curve (AROC), suggest the efficacy of our proposed schemes across many applications that require person identification and verification.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals Open-set Face Recognition for Small Galleries Using Siamese Networks

2020 ◽  
Author(s):  
Gabriel Salomon ◽  
Alceu Britto ◽  
Rafael H. Vareto ◽  
William R. Schwartz ◽  
David Menotti
Keyword(s):  
Face Recognition ◽  
Open Set ◽  
Siamese Networks

An open mapping theorem for o-minimal structures

2001 ◽  
Vol 66 (4) ◽  
pp. 1817-1820 ◽  
Author(s):  
Joseph Johns
Keyword(s):  
Elementary Proof ◽  
Ordered Set ◽  
Cartesian Product ◽  
Closed Field ◽  
Product Topology ◽  
Open Mapping Theorem ◽  
Open Set ◽  
Minimal Structure ◽  
Background Material ◽  
Linearly Ordered Set

We fix an arbitrary o-minimal structure (R, ω, …), where (R, <) is a dense linearly ordered set without end points. In this paper “definable” means “definable with parameters from R”, We equip R with the interval topology and Rn with the induced product topology. The main result of this paper is the following.Theorem. Let V ⊆ Rnbe a definable open set and suppose that f: V → Rnis a continuous injective definable map. Then f is open, that is, f(U) is open whenever U is an open subset of V.Woerheide [6] proved the above theorem for o-minimal expansions of a real closed field using ideas of homology. The case of an arbitrary o-minimal structure remained an open problem, see [4] and [1]. In this paper we will give an elementary proof of the general case.Basic definitions and notation. A box B ⊆ Rn is a Cartesian product of n definable open intervals: B = (a1, b1) × … × (an, bn) for some ai, bi, ∈ R ∪ {−∞, +∞}, with ai < bi, Given A ⊆ Rn, cl(A) denotes the closure of A, int(A) denotes the interior of A, bd(A) ≔ cl(A) − int(A) denotes the boundary of A, and ∂A ≔ cl(A) − A denotes the frontier of A, Finally, we let π: Rn → Rn− denote the projection map onto the first n − 1 coordinates.Background material. Without mention we will use notions and facts discussed in [5] and [3]. We will also make use of the following result, which appears in [2].

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