Deep Learning based approach to detect Customer Age, Gender and Expression in Surveillance Video

Ijjina, Earnest Paul; Kanahasabai, Goutham; Joshi, Aniruddha Srinivas

doi:10.1109/icccnt49239.2020.9225459

Cited by 15 publications

(6 citation statements)

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“…Analyzing customer attitudes and behavior in a retail store can help maximize profits and enhance a retailer's competitiveness. Thus, some interesting architectures were adopted to ease the customer detection [12], [52], [53], [54]. In [12], the deep learning-based framework enabled to identify faces (using the Haar Cascade object detection model), gender and age (using Wide ResNet 16-8 with a 64x64 RGB image as input for the network), and facial expressions of customers (using the mini Xception model).…”

Section: Related Computer Vision Workmentioning

confidence: 99%

“…Thus, some interesting architectures were adopted to ease the customer detection [12], [52], [53], [54]. In [12], the deep learning-based framework enabled to identify faces (using the Haar Cascade object detection model), gender and age (using Wide ResNet 16-8 with a 64x64 RGB image as input for the network), and facial expressions of customers (using the mini Xception model). Similarly, [55] implemented a system to detect faces, estimate age and gender of customers, and track them into the shopping area.…”

Section: Related Computer Vision Workmentioning

confidence: 99%

“…This technique can be deployed on embedded devices such as Raspberry Pi, which have the necessary capabilities. Special environmental conditions for operating the computer vision solutions were described in [12] through its framework to operate under poor illumination, varying angles of view, and over-exposure of the background. In many cases conventional cameras for CCTV were used, but with fisheye cameras were also achieved notable results.…”

Section: Related Computer Vision Workmentioning

confidence: 99%

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Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Fernández Del Carpio

2024

Int. J. Adv. Intell. Informatics

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Computer vision has become an important technology for obtaining meaningful data from visual content and providing valuable information for enhancing security controls, marketing, and logistic strategies in diverse industrial and business sectors. The retail sector constitutes an important part of the worldwide economy. Analyzing customer data and shopping behaviors has become essential to deliver the right products to customers, maximize profits, and increase competitiveness. In-person shopping is still a predominant form of retail despite the appearance of online retail outlets. As such, in-person retail is adopting computer vision models to monitor store products and customers. This research paper presents the development of a computer vision solution by Lytica Company to detect customers in Steren’s physical retail stores in Mexico. Current computer vision models such as SSD Mobilenet V2, YOLO-FastestV2, YOLOv5, and YOLOXn were analyzed to find the most accurate system according to the conditions and characteristics of the available devices. Some of the challenges addressed during the analysis of videos were obstruction and proximity of the customers, lighting conditions, position and distance of the camera concerning the customer when entering the store, image quality, and scalability of the process. Models were evaluated with the F1-score metric: 0.64 with YOLO FastestV2, 0.74 with SSD Mobilenetv2, 0.86 with YOLOv5n, 0.86 with YOLOv5xs, and 0.74 with YOLOXn. Although YOLOv5 achieved the best performance, YOLOXn presented the best balance between performance and FPS (frames per second) rate, considering the limited hardware and computing power conditions.

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Section: Related Computer Vision Workmentioning

confidence: 99%

Section: Related Computer Vision Workmentioning

confidence: 99%

Section: Related Computer Vision Workmentioning

confidence: 99%

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Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Fernández Del Carpio

2024

Int. J. Adv. Intell. Informatics

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“…In the application of fatigue driving (Abbas et al, 2022; Xiao et al, 2022), if the in‐vehicle detection system detects signs of fatigue driving in the driver's facial expressions, it will emit an alarm to remind the driver. In sales applications (Ijjina et al, 2020), customers' facial expressions are essential data for computers to determine whether they need a human sales assistant. In classroom teaching (Pabba & Kumar, 2022; Tang et al, 2020; Tonguç & Ozkara, 2020), intelligent systems collect students' facial expressions and analyse expressions such as boredom, confusion, focus, yawning, and fatigue using facial expression recognition models to determine students' current learning states.…”

Section: Introductionmentioning

confidence: 99%

A multi‐scale feature fusion convolutional neural network for facial expression recognition

Zhang,

Fu,

et al. 2023

Expert Systems

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This paper designs a new facial Expression recognition network called a multi‐scale feature Fusion Convolutional neural Network (EFCN). This network is proposed to solve two problems in the facial expression recognition task. First, there are many commonalities between faces of different expression categories, and the recognition task cannot be precisely performed when the commonality is greater than the individuality. Secondly, facial detail features have a significant impact on the final results of expression recognition, while the image detail features extracted by traditional convolutional neural networks are not sufficient. In order to address the above issues, the feature enhancement network (FEN) and the detail information enhancement module (DEM) are designed. The FEN fuses deep and shallow features. Accordingly, the feature map contains richer information, making it easy to identify the samples. The DEM extracts and fuses the features passed by the backbone network with multi‐scale features to enhance the network's ability to extract features from small regions of the face. We validated the proposed method on three datasets, RAF‐DB, CK+, and JAFFE, and achieved 84.50%, 97.86%, and 91.05% accuracy, respectively, and the experimental results showed the effectiveness of the proposed method in this paper. For example, on the JAFFE dataset, the recognition accuracy of this method surpasses the MLT method by 1.87%.

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“…Today face recognition algorithms are implemented in a wide range of products and solutions. People counting cameras are used to estimate the number of clients getting into a store and draw some statistical conclusions on their characteristics, e.g., age or gender [2]. Most mobile phones in the market have embedded technology to unlock them with a simple look at the device [3].…”

Section: Introductionmentioning

confidence: 99%

Stegano-Morphing: Concealing Attacks on Face Identification Algorithms

Carabe

Cermeño²

2021

IEEE Access

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Face identification is becoming a well-accepted technology for access control applications, both in the real or virtual world. Systems based on this technology must deal with the persistent challenges of classification algorithms and the impersonation attacks performed by people who do not want to be identified. Morphing is often selected to conduct such attacks since it allows the modification of the features of an original subject's image to make it appear as someone else. Publications focus on impersonating this other person, usually someone who is allowed to get into a restricted place, building, or software app. However, there is no list of authorized people in many other applications, just a blacklist of people no longer allowed to enter, log in, or register. In such cases, the morphing target person is not relevant, and the main objective is to minimize the probability of being detected. In this paper, we present a comparison of the identification rate and behavior of six recognizers (Eigenfaces, Fisherfaces, LBPH, SIFT, FaceNet, and ArcFace) against traditional morphing attacks, in which only two subjects are used to create the altered image: the original subject and the target. We also present a new morphing method that works as an iterative process of gradual traditional morphing, combining the original subject with all the subjects' images in a database. This method multiplies by four the chances of a successful and complete impersonation attack (from 4% to 16%), by deceiving both face identification and morphing detection algorithms simultaneously.

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Deep Learning based approach to detect Customer Age, Gender and Expression in Surveillance Video

Cited by 15 publications

References 10 publications

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

Analyzing computer vision models for detecting customers: a practical experience in a mexican retail

A multi‐scale feature fusion convolutional neural network for facial expression recognition

Stegano-Morphing: Concealing Attacks on Face Identification Algorithms

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