Introduction to People Detection and Face Recognition

What is Object Detection:

Object detection is a computer vision and image processing technique for identifying and locating objects in an image or video. Object detection can be used to count objects in a scene, determine and monitor their precise positions, and accurately mark them with this type of detection and localization.

What is People Detection:

Detecting and identifying the location of People in an image or video is called People Detection. We called it Person detection, which is a task that the computer vision framework takes on to find and trach people.

What is Face Recognition:

Facial recognition is a method of recognizing or verifying a person’s identification by looking at an image. It works by pinpointing and measuring facial features from a given image by comparing them with a database of faces.

Benefits of People Detection and Face Recognition

Human detection in real-time is becoming a major trend among data scientists and across a broad range of industries, from smart cities to retail to surveillance. The scenarios like counting the number of pedestrians on a street or at a crosswalk, analyzing the actions of the customers or the time they spend at any particular location, and visitors or intruders being detected by the home security camera have become a reality in modern days.

A variety of sectors, including banking, insurance, manufacturing, and others, are using computer vision to improve consumer delight and satisfaction. Organizations that use AI as an ideology currently compel us to inspire companies to offer better services to their customers.

Facial recognition technology has a wide range of business applications. Here are a few ways that facial recognition technology can help businesses.

• Privacy
• Security
• Payments
• Employee Productivity

People Detection Methodology

Taking a visual form of a visual surveillance system and accurately identifying human beings (people) is crucial due to many reasons such as people’s posture, pose, background lightning, brightness, weather condition, identification at night, collusion with other objects, and camera location.

People identification using computer vision accomplishes following tasks,

• Objects are selected from background images.
• Using a probability ranking, assigns the items to a specific class — in this case, humans.
• With x-y roots and height and length meanings, defines the proposed people’s boundaries.

In general, a systematic division of the image is the first step in solving the problem of how to detect objects in a picture or video data. To begin, the tool will use algorithms to analyze data to classify regions of interest. The computer will then generate several object suggestions based on your preferences. The last steps in the detection process are to identify objects using templates, apply probability thresholds, and return the classes and positions in the context of your final approved proposals. In this scenario, the class you’re searching for is humans. The applications use computing blocks that have been pre-trained by crunching a large number of images with a deep learning artificial intelligence algorithm to detect these human artifacts in the visual field. Models are these computing blocks that can be learned to understand almost everything humans can see.

People Detection techniques (Algorithms for object detection)

Methods for object detection generally fall under either neural network-based or non-neural approaches. For non-neural approaches, it becomes necessary to first define features using one among the methods below, employing a technique like support vector machine (SVM) to try the classification. Most of the time deep learning-based techniques work easier, faster, and accurately better in people detection.

Face Recognition Methodology

Computer vision systems may recognize people’s gender, age, emotions, and cultural appearance. In biometric authentication systems, facial recognition is used.

Object detection frameworks are used in both face recognition and facial detection to identify and locate objects in a visual field. Facial recognition will take some kind of image data, search for people or faces, and locate them in the picture. In addition, facial recognition will identify eyes, mouths, and other features to compare against an established dataset.

Detection/tracking, synchronization, feature extraction, and feature matching and classification are the four stages of facial recognition technology. Facial recognition recognizes and monitors individuals in a given image or video file during the detection/tracking stage. The alignment stage tells you where the face lines are in the picture or video file you’re working with. It also includes information on the contours of the facial features.

People Detection & Face recognition – Rhino Experience

Amazon Web Services(AWS) provides a unique API named “AWS Rekognition” to detect the labels, faces. This API was able to identify these features in a stored image or video or a streaming video. Based on the desired confidence interval, it retrieved the identification labels.

Amazon Rekognition API provides a separate API to detect the faces in a given image or video. With this functionality, it was able to retrieve where the face was detected using a coordinate system, the facial expressions of the identified image, and the position of facial landmarks also. When you pass an image for this face detection functionality of AWS Rekognition API, it returns where the face was detected and expressions with a confidence interval which can be determined by the wish of the analyzer.

There are two basic aspects of face recognition in machine learning. They are face identification and face recognition. This identification can be used to develop a machine learning model to answer the following questions.

• Is there a person in the image?
• Is this the person we want?

The following features can be retrieved by face recognition functionality.

• Emotions
• Landmarks
• Quality
• Pose

Computer Vision People Detection & Face Recognition Tools

These tools can be used for Computer Vision projects to object detection and object recognition purposes. You may need to integrate some of these when it comes to a complex task.

OpenCV

OpenCV is a cross-platform library that can be used to build real-time computer vision apps. It focuses primarily on image processing, video recording, and analysis, with features such as face detection and object detection.

Simple CV

SimpleCV is a free and open-source platform for creating computer vision apps. You can use powerful computer vision libraries, such as OpenCV, without learning how to use them. This is computer vision at the most basic level.

Boof CV

BoofCV is a real-time computer vision open source library written from the ground up. Low-level image processing, camera calibration, feature detection/tracking, structure-from-motion, fiducial detection, and recognition are all covered by their capabilities. BoofCV is available for both academic and commercial use under an Apache 2.0 license.

Tensorflow

TensorFlow is a machine learning software library that is free and open-source. It can be used for a variety of activities, but it focuses on deep neural network training and inference. Google developer team product.

Keras

Keras is an open-source software library for artificial neural networks that offers a Python interface. Keras serves as a user interface for TensorFlow.

CUDA

Algorithms for computer vision and image processing are computationally intensive. Applications can achieve interactive video frame-rate efficiency with CUDA acceleration. We’ll go through some of the work in the field of imaging and vision, as well as some developer tools.

YOLO (You only look once)

Computer vision algorithms that are quicker and more reliable. To predict what objects are present and where they are present, you just look at a picture once (YOLO).

CVLib

A Python Computer Vision library that is plain, high-level, and easy to use. It was created to make experimentation simple and fast. It is critical to be able to move quickly from a concept to a prototype to conduct an effective study.

Cascade Classifier

A tool to create your image classification model. Get image data as positive and negative sets, then use one of the below tools to get work done.

• Cascade Trainer GUI
• MakeSense
• VGG Image Annotation (VIA)

APIs

Computer vision APIs enable devices to identify patterns and images in the real world, and they have a wide range of applications in our everyday lives. We already live in a world where such devices can communicate with each other and respond to logical commands.

• AWS Rekognition API
• Microsoft Azure Computer Vision API
• Google Cloud Platform (GCP) Vision AI
• IBM Watson

Real-World Scenarios of Computer Vision People Detection & Face Recognition

These are modern world business-level examples that deal with deep learning and computer vision.

• Employee attendance using face recognition – Facial Biometrics.
• Frequent customers & new customer identification – Face recognition.
• Banking (Creating Virtual accounts) – Face recognition.
• Facial expression recognition.
• ID verification (sensitive information) – Text Recognition/ Face recognition.
• Customer Identification (KYC – Know Your Customer) to provide a personalized customer experience – Facial Recognition.
• Secure access to places like schools, airports, and offices – Object Detection & Motion Detection.
• Self-driving vehicles – People detection.
• Automated drone – People detection / Face Recognition.
• Forensic investigation – Advanced Facial Recognition.
• Face search and verification – Face recognition.
• Traffic management system – People detection.

How Rhino does it?

We are happy to announce that as the Rhino-Partners Data Science team, we are very much capable enough to work with these technologies and tools. You will experience our widespread domain knowledge and exposure to business processes, consumer behavior, and product performance combined with our specialization in scientific approaches for unveiling hidden intelligence from data. Our Deep Learning & Computer Vision discipline is more focused on producing the desired outcomes that best suits the business nature, in terms of KPIs or the tool that offers the best advantage to the client.