Computer Vision, a subset of Artificial Intelligence is now become the talk of the town as it empowers computers to analyze and comprehend the visual world more efficiently. Currently, it has become immensely popular in dynamic industries such as retail, insurance, banking, hospitality, and manufacturing. These sectors are adopting machine vision to enhance their customer experience, save time, reduce effort, and attain superior quality assurance.
Computer Vision enables machines or devices to perceive, detect, identify, and label objects in a manner similar to humans. While humans observe an image, they comprehend a wealth of additional information apart from the primary object. An effective computer vision system is capable of recognizing objects and their attributes such as shape, color, size, texture, spatial arrangement, background objects, and more.
Facial recognition supports recognizing human facial features in an image by using computer vision. Classical computer vision techniques have used “Haar-like features” to analyze the parts between facial features, but modern facial recognition applications depend on Artificial Intelligence. It helps in object recognition.
Until recently, facial recognition technology was restricted to researchers and security professionals. However, it now has significant applications and usage in the business world. Facial recognition is a form of applied machine learning that aims to detect and identify human facial features. The proliferation of facial recognition technologies on smartphones, tablets, and laptops has already been witnessed.
Facial recognition is revolutionizing retail stores in various ways:
- Identity verification
- Security and access control
- Employee tracking
- Boost In-Store Customer Service with Personalised Assistance
- Prevention of Theft and Shoplifting
With the trendy next-generation facial recognition software, it is even possible to look at gestures, postures, and facial expressions to determine whether a customer has bad intentions or not in a retail store or other places. Gait analysis is the same kind of security software. It is also helpful to detect those criminals who hide their faces intentionally by wearing masks. Facial emotion recognition (FER) is the subset of facial detection that drives facial expression detection. It categorizes expressions into different categories like unhappy, joy, anger, surprise, etc.
These networks’ positive outcomes are due to the availability of humongous storage space and raw processing capabilities made possible by Infrastructure-as-a-Service (IaaS) providers through cloud computing. Presently, facial recognition tech is at a stage where humans cannot match up in identifying faces, particularly if the task consists of a considerable number of randomized faces.
Facial recognition is a tech that carries out biometric identification or verification through examination of the subject’s facial traits. To perform this, facial recognition might leverage 3D facial recognition, consult 2D imagery, or look at particular algorithms that relate to the distances between features.
Facial recognition technologies function in four phases: detection and tracking, alignment, feature extraction, and feature correlation and categorization. In the detection/tracking phase, facial recognition tech goes about identifying and tracking a provided image or video file. The alignment phase informs where the face lines are in the provided image or video file. It additionally details the contours of the facial features.
Gartner Insights regarding Retail Technology
“According to the 2022 Gartner Technology Buying Behavior Survey, retail buyers are using a wide range of third-party resources to help them make more informed purchase decisions. 2 These resources include working with consultants, speaking with analysts and thought leaders, reviewing published research, and attending industry events.
On average, retail buyers tapped into about three third-party resources during their decision process, but two in five (40%) used four or more resources. The range of third-party resources buyers are using underscores the importance of having a robust influencer strategy and program that recognises not only traditional third-party influencers but also the influence and importance of customer advocacy programs and partners.”
Common Applications of Computer Vision in the Retail Sector
Object Classification: These are objects, categorized into common and wide categorizations. For example, they can differentiate the provided input image as a living thing, such as an animal or a human, or as an automobile or a building. They can go in-depth with further classification of animals into tigers, crocodiles, and owls. They can classify objects as bridges, buildings, and churches.
- Object Identification: It deals with the identification of the object within the image.
- Object verification/authentication: This consists of checking if the required object is present in the image that serves as input to the computer vision system.
- Object detection: This handles determining the location of a particular object within the image, and this is only possible with computer vision frameworks.
- Object recognition: A common image would have several objects. Computer vision systems/frameworks can carry out recognition of all of these objects and their locations within the image with a high degree of precision and accuracy.
- Object tracking: We can go about tracking a particular object across an array of imagery. This object could range the gamut from a human being to an automobile, and we can undertake tracking of them leveraging computer vision frameworks spread throughout an area, which can clash with ethics in some scenarios. For example, tracking a military drone or a state-owned vehicle.
- Object counting: Computer vision systems can deconstruct any object into the sum of its parts, which assists in the identification of different varieties of imagery of the same object. An ideal example is people possessing differing physical features and dresses. It is greatly beneficial for counting and has been a massive asset in limiting the number of people in a closed region during this COVID-19 pandemic.
- Recognition: Computer vision systems/frameworks can detect the gender, age, emotions, and cultural appearance of individuals. Facial recognition has been identified for use in biometric identification systems.
- Action recognition: Computer vision systems can detect the action or gesture of an individual or animal present in the image.
- Predicting behaviour: Computer vision systems can research the mood and sentiments of individuals within the image and predict their reactions to fresh situations on that basis.
- Object Character Recognition (OCR): Computer vision systems/frameworks can detect the text and numbers written within an image. This is being broadly leveraged to produce apps that extract data through uploads of photos of things such as business cards.
- Document Analysis: Computer vision systems can undertake the analysis of any document.
How to leverage Computer Vision in the retail industry?
Computer Vision technology plays a pivotal role in theft prevention in any retail store. Strategically located computer vision systems can assist in theft-proofing measures.
Computer vision systems deal with broad deployment, acceptance, and implementation within the retail industry.
Here are some methods by which retailers can leverage computer vision:
- It helps to determine human traits like age or gender to comprehend the client demographics.
- It helps track each client’s movements throughout the store and obtain insights into product visibility and the efficacy of aisle arrangements.
- Eye movement, facial expressions, and other hand gestures help to identify every human being, and they also help to isolate the products that attract the most clients, regardless of whether they bought them or not.
- Computer vision algorithms can produce a precise picture of inventory, integrated with product management frameworks to place orders.
The advantages that facial recognition technology confers upon the organizations
There are several business deployments of facial recognition tech. The following are a few of the ways facial recognition tech can provide advantages to enterprises.
End-users of the multimedia messaging application Snapchat can now select their own privacy preferences for photos owing to the enterprise’s leveraging of facial recognition tech. The facial recognition framework of Snapchat scans faces in imagery to decide which ones to block to enforce privacy.
The facial recognition framework leverages an emoji to block out the chosen faces. Snapchat’s facial recognition becomes important when the account of an end-user is configured to a particular setting. This technology is important to exist in the latest retail industry as it helps safeguard the privacy of every individual.
Smart Lock, a facial recognition application that Android users can use on their smartphones, enables smartphone end-users to unlock their phones by holding them to their faces. iPhone users have the same convenience by leveraging the Face ID app produced by Apple for iOS. Through harnessing this tech, enterprises can safeguard sensitive data on their devices.
The Chinese e-commerce heavyweight Alibaba is presently working towards the integration of AliPay, its payment service, with facial recognition feature sets. Integrating AliPay with face recognition implies that clients can now purchase from Alibaba without having to furnish their card details or other personal data. This is really a revolutionary advancement, as it will help minimize the risks of theft and fraud for enterprises all over the globe.
We can observe that there is a big change in the productivity of any organization, when the staff is not performing their jobs with complete involvement. They might chat with colleagues when the supervisor is out of sight or they might use their work hours to indulge in non-productive activities.
If the supervisor or manager is on leave and there is no substitute doing their job, they might even go to the extent of falsifying attendance by asking their friends to use their login cards while they stay at home. Unless a manager deploys 24x7x365 surveillance on production teams, they might be blissfully unaware of such abuses. The best course of action is to leverage facial recognition tech for staff attendance. With this resolution, supervisors and managers have peace of mind knowing that the attendance records are accurate and the store is fully theft-free. In this way, a staff member is ‘present’ only for that time when they are physically within the work premises.
Not just that, facial recognition tech that is set up on the workstations of individual staff members will facilitate knowing how many hours they were actually productive and how much time was wasted. Even though we might have to wait for a few more years, perhaps even more, prior to the deployment of facial recognition tech in workplaces becoming commonplace, the practice will totally alter how staff members work, streamlining and maximizing productivity in the process.
Leading Computer Vision Tools
As specified prior, cloud tech has played a critical role in the widespread acceptance and deployment of computer vision technologies. It is needless to add that dominant cloud services providers such as Microsoft, Google, and IBM have their own proprietary computer vision solutions. There are tons of open-source utilities available for the development of computer vision systems.
OpenCV is the most widespread library of optimized programming functionalities in the pursuit of developing solutions for real-life problems. The library has cross-platform functionality and is open source. It features integration with C++ and Python, which renders it a lucrative option for beginners as well, individuals who are not adept at harnessing these technologies and the associated software.
This is a framework for open-source machine vision leveraging the OpenCV library and Python as the programming language. It is for casual end-users who possess no experience in authoring programs. Cameras, images, video streams, and video files are interoperable on SimpleCV and manipulations are very quick.
This is the most widespread deep learning library due to the simplicity and accessibility of its API. It is a free, open-source library for data streams and differential programming. TensorFlow 2.0 is compatible with picture and speech recognition, object detection/identification, reinforced learning, and recommendations. Its reference model makes it simpler and more accessible to begin the development of solutions.
MATLAB is a multi-paradigm numerical computational environment and proprietary programming language produced by MathWorks. It facilitates matrix manipulation, the plotting of functions and data, the development of user interfaces, and the implementation of algorithms. It also facilitates integration with applications authored in other languages.
CUDA is a parallel computing and application programming interface model developed by nVidia, the market leader in graphics processing units. It provides amazing performance by leveraging the GPU. nVidia Performance Primitives Library is a part of CUDA and consists of a grouping of image, signal, and video processing functionalities.
Keras is an open-source neural network library produced in Python. It has undergone optimization to minimize cognitive load and focuses on being accessible, user-friendly, modular, and extensible. It can also function on top of Microsoft Cognitive Toolkit, TensorFlow, R, PlaidML, or Theano.
You Look Just Once is an object identification framework for real-time processing. It is a sophisticated real-time object detection system.
BoofCV is an open-source Java library, authored from the ground up for real-time robotics and computer vision applications for both academic and enterprise deployment. It includes functionalities like low-level image processing, feature detection, and tracking, camera calibration, classification, and recognition.
Gartner Insights for Retailers to Manage a Successful Retail Business
Gartner said, “Retailers strongly prefer to use consultancies to develop their requirements as well as to manage the vendor selection process. This finding supports Gartner’s other buying process research showing that many buyers are overwhelmed and more likely to engage with providers and sellers that can help them make sense of the information they have during the decision process.”
He added, “Business Consultancies help retailers build strategies.” Business consulting firms advise enterprises on how to optimize their operating models or transform their business models. They have thought leadership subsidiaries or practices in addition to select advisory services offered to customers.”
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