Customer and target individual face analysis for retail analytics

Karim, Nabil Tahmidul; Jain, Sanjana; Moonrinta, Jednipat; Dailey, Matthew N.; Ekpanyapong, Mongkol

doi:10.1109/iwait.2018.8369732

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Deep learning is also employed for automation of customer analysis. Applications have included the use of deep learning to perform customer density mapping within stores [302], customer tracking in video [303], detection of customer gaze upon products on shelves [304,305], detection of shopper demographics and emotion recognition from facial expression [306], and even association of product detection with customer pose detection to determine customer purchase behavior [307]. The use of deep learning outdoors, on retail high streets, is less prevalent in the existing literature; however, a number of applications have been introduced.…”

Section: Deep Streetscapesmentioning

confidence: 99%

Smart and Sentient Retail High Streets

Torrens

2022

Smart Cities

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Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public.

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Section: Deep Streetscapesmentioning

confidence: 99%

Smart and Sentient Retail High Streets

Torrens

2022

Smart Cities

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“…Real-time adjustments can sometimes be made based on the emotional input received from the audience [16]. Consider interactive presentations or live performances [15]. Presenters can use this technology to help them modify their style in order to maintain audience interest [9] [27].…”

Section: Introductionmentioning

confidence: 99%

Feedback Analyser through Facial Expressions

Chandra,

Reddy

2024

IJSREM

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Abstract—Feedback is an essential part of human connection and communication since it may used to express expression, feelings, and truth. This research paper works on the complex function that facial signals play in the interpretation and communication of information by offering a thorough overview and analysis of feedback through facial expressions. We investigate the brain systems that underlie facial feedback perception and response in humans, as well as the mechanisms behind face expressions, cultural variances, and their universality. Deep neural networks facial expression feedback analyzers are cur- rently in the starting stages of development, but they have the strength to solve a variety of issues with present human- computer interface systems .Feedback detection, for instance, is used to build more immersive entertainment experiences, more natural and educational systems, and more in customer service experiences, everywhere feedback is used. One of the main advantages of feedback analyzers using face expression by deep neural networks is that they can provide real time feedback. This is in way of natural feedback collection methods, such as surveys and interviews, which can be time consuming and expensive to administer. Real time feedback analyzer allows system developers to make rapid changes to their system in order to improve and develop the user experience. we have leveraged a custom trained CNN model, to accurately classify the seven different emotions of an human face that are availed in the dataset. The model has achieved an impressive accuracy of 83%. This model can be used in various applications. Index Terms—Convolutional Neural Networks, facial expres- sion, emotion, feedback, Resnet.

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