Similar to a fingerprint search system, face recognition technology can assist law enforcement agencies in identifying suspects or finding missing persons. Face recognition technology lets the police detect a suspect's face and compare it with image databases of known criminals and provides investigators with a match list of the most similar faces. Face recognition is a highly efficient and accurate tool in investigation processes. However, in some sensitive scenarios covert methods are required for the detection of suspects or missing persons without risking the lives of police. With the availability of the nano devices such as Raspberry Pi, law enforcement agencies such as police can be equipped with a concealed and secure face recognition system. In this paper, a Raspberry Pi and cloud assisted face recognition framework is proposed. A small-sized portable wireless camera is mounted on a police officer's uniform to capture a video stream, which is passed to Raspberry Pi for face detection and recognition. The proposed method uses Bag of Words for extraction of oriented FAST and rotated BRIEF points from the detected face, followed by support vector machine for identification of suspects. Raspberry Pi has limited resources such as storage space, memory, and processing power, and therefore the proposed classifier is stored and trained on the cloud. The proposed method is implemented on Raspberry Pi 3 model B in Python 2.7 and is tested on various standard datasets. Experimental results validate the efficiency of the proposed method in accurate detection of faces compared to state-of-the-art face detection and recognition methods, and verify its effectiveness for enhancing law-enforcement services in smart cities. © 2017 Elsevier B.V.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Sajjad M

Nasir M

Muhammad K

Khan S

Jan Z

Elhoseny M

Baik S.W.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Future Generation Computer Systems

Multimedia Tools and Applications

Parameter identification of two dimensional digital filters using electro-magnetism optimization

Many hybrid and multimodal biometric recognition techniques have been presented to provide secure and authentic systems, incorporating both soft and hard biometric schemes. This article proposes a new hybrid technique which ensures the authenticity of the user to the system, as well as monitors whether the user has passed the biometric system as a normal or spoofed one. The proposed scheme is two-fold: Tier I integrates fingerprint, palm vein print and face recognition to match with the corresponding databases, and Tier II uses fingerprint, palm vein print and face anti-spoofing convolutional neural networks (CNN) based models to detect spoofing. In first stage, the hash of a fingerprint is compared with the fingerprint database. After a successful match of the fingerprint, it is tested on a CNN-based model of the fingerprint to verify whether it is a spoof or real. A similar process is repeated for the palm and face, and based on collective evidence, the system permits the user to login the system. Experimental results over five benchmark datasets verified the effectiveness of the proposed system in providing efficient and robust verification, overcoming the limitations in normal authentication and spoofing practices. © 2018 Elsevier B.V.

Pattern Recognition Letters

CNN-based anti-spoofing two-tier multi-factor authentication system

In smart cities, traffic management is becoming a significant challenge owing to the rapid growth of population. In this regard, pedestrian detection and their safety are of the utmost importance for the city authorities. In this paper, a fully convolutional deep architecture is presented to detect pedestrians by automatically selecting the desired region proposals as well as learning the requisite feature representation with no need for any manual hand-crafted feature design. The architecture facilitates end-to-end training and thereby improves the overall performance, eliminating the bottleneck caused by the multistage pipeline structure of conventional feature engineering. A state-of-the-art deep framework, for general object detection, is suitably tailored for the task of pedestrian detection. A densely connected convolutional network is employed to learn the desired features. A two-stage approach is proposed to separate the human-look-alike hard negative backgrounds from the true pedestrians. Besides, feature maps from multiple intermediate layers are taken into consideration to facilitate small-scale detection. The proposed method alongside a few competent schemes is compared on the benchmark Caltech and INRIA datasets, where the log average miss rate is set as the performance metric. The obtained results demonstrate the potential of our approach in addressing the real-world challenges. © 2018 John Wiley & Sons, Ltd.

Transactions on Emerging Telecommunications Technologies

Scale Aware Deep Pedestrian Detection

Facial expression recognition is an active research area for which the research community has presented a number of approaches due to its diverse applicability in different real-world situations such as real-time suspicious activity recognition for smart security, monitoring, marketing, and group sentiment analysis. However, developing a robust application with high accuracy is still a challenging task mainly due to the inherent problems related to human emotions, lack of sufficient data, and computational complexity. In this paper, we propose a novel, cost-effective, and energy-efficient framework designed for suspicious activity recognition based on facial expression analysis for smart security in law-enforcement services. The Raspberry Pi camera captures the video stream and detects faces using the Viola Jones algorithm. The face region is pre-processed using Gabor filter and median filter prior to feature extraction. Oriented FAST and Rotated BRIEF (ORB) features are then extracted and the support vector machine (SVM) classifier is trained, which predicts the known emotions (Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise). Based on the collective emotions of the faces, we predict the sentiment behind the scene. Using this approach, we predict if a certain situation is hostile and can prevent it prior to its occurrence. The system is tested on three publically available datasets: Cohen Kande (CK+), MMI, and JAFEE. A detailed comparative analysis based on SURF, SIFT, and ORB is also presented. Experimental results verify the efficiency and effectiveness of the proposed system in accurate recognition of suspicious activity compared to state-of-the-art methods and validate its superiority for enhancing security in law enforcement services. © 2018 Elsevier Inc.

Information Sciences

Raspberry Pi assisted facial expression recognition framework for smart security in law-enforcement services

With the prevalent use of mobile cameras to capture images, the demands for efficient and effective methods for indexing and retrieval of personal image collections on mobile devices have also risen. In this paper, we propose to represent images with hash codes, which is a compressed representation of deep convolutional features using deep auto-encoder on the cloud. To ensure user's privacy, the image is first encrypted using a light-weight encryption algorithm on mobile device prior to offloading it to the cloud for features extraction. This approach eliminates the computationally expensive process of features extraction on resource constrained devices. A pre-trained convolutional neural network (CNN) is used to extract features which are then transformed to compact binary codes using a deep auto-encoder. The hash codes are then sent back to the mobile device where they are stored in a hash table along with image location. Approximate nearest neighbor (ANN) search approach is utilized to efficiently retrieve the desired images without exhaustive searching of the entire image collection. The proposed method is evaluated against three different publicly available image datasets namely Corel-10K, GHIM-10K, and Product image dataset. Experimental results demonstrate that features representation using CNN and auto-encoder shows much better results than several state-of-the-art hashing schemes for image retrieval on mobile devices. © 2018 Elsevier B.V.

Computer Communications

Privacy-preserving image retrieval for mobile devices with deep features on the cloud

IEEE Transactions on Circuits and Systems II: Express Briefs

Dynamic Load Balancing and Reactive Power Compensation Switch Embedded in Power Meters

Mobile-cloud assisted framework for selective encryption of medical images with steganography for resource-constrained devices

2017 International Conference on Electrical, Computer and Communication Engineering (ECCE)

An IoT based fire alarming and authentication system for workhouse using Raspberry Pi 3

Physics Today

Preprint repository arXiv achieves milestone million uploads

IEEE Access

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities

Journal	Data powered by TypesetFuture Generation Computer Systems
Publisher	Data powered by TypesetElsevier BV
ISSN	0167-739X
Open Access	0