Vision: Instructional Materials


How to Develop a Face Recognition System Using FaceNet in Keras

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Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to multiple third-party open source implementations of the model and the availability of pre-trained models. The FaceNet system can be used to extract high-quality features from faces, called face embeddings, that can then be used to train a face identification system. In this tutorial, you will discover how to develop a face detection system using FaceNet and an SVM classifier to identify people from photographs. How to Develop a Face Recognition System Using FaceNet in Keras and an SVM Classifier Photo by Peter Valverde, some rights reserved. Face recognition is the general task of identifying and verifying people from photographs of their face.


Building a Chat Bot With Image Recognition and OCR

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In part 1 of this series, we gave our bot the ability to detect sentiment from text and respond accordingly. But that's about all it can do, and admittedly quite boring. Of course, in a real chat, we often send a multitude of media: from text, images, videos, gifs, to anything else. So in this, our next step in our journey, let's give our bot vision. The goal of this tutorial is to allow our bot to receive images, reply to them, and eventually give us a crude description of the main object in said image.


Building a Chat Bot With Image Recognition and OCR

#artificialintelligence

In part 1 of this series, we gave our bot the ability to detect sentiment from text and respond accordingly. But that's about all it can do, and admittedly quite boring. Of course, in a real chat, we often send a multitude of media: from text, images, videos, gifs, to anything else. So in this, our next step in our journey, let's give our bot vision. The goal of this tutorial is to allow our bot to receive images, reply to them, and eventually give us a crude description of the main object in said image.


A Gentle Introduction to Deep Learning for Face Recognition

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Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades until recently. Deep learning methods are able to leverage very large datasets of faces and learn rich and compact representations of faces, allowing modern models to first perform as-well and later to outperform the face recognition capabilities of humans. In this post, you will discover the problem of face recognition and how deep learning methods can achieve superhuman performance.


How to Perform Object Detection With YOLOv3 in Keras

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Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. In recent years, deep learning techniques are achieving state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Notable is the "You Only Look Once," or YOLO, family of Convolutional Neural Networks that achieve near state-of-the-art results with a single end-to-end model that can perform object detection in real-time. In this tutorial, you will discover how to develop a YOLOv3 model for object detection on new photographs.


A Gentle Introduction to Object Recognition With Deep Learning

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The model is significantly faster to train and to make predictions, yet still requires a set of candidate regions to be proposed along with each input image. Python and C (Caffe) source code for Fast R-CNN as described in the paper was made available in a GitHub repository. The model architecture was further improved for both speed of training and detection by Shaoqing Ren, et al. at Microsoft Research in the 2016 paper titled "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." The architecture was the basis for the first-place results achieved on both the ILSVRC-2015 and MS COCO-2015 object recognition and detection competition tasks. The architecture was designed to both propose and refine region proposals as part of the training process, referred to as a Region Proposal Network, or RPN. These regions are then used in concert with a Fast R-CNN model in a single model design. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance.


Liveness Detection with OpenCV - PyImageSearch

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In this tutorial, you will learn how to perform liveness detection with OpenCV. You will create a liveness detector capable of spotting fake faces and performing anti-face spoofing in face recognition systems. How do I spot real versus fake faces? Consider what would happen if a nefarious user tried to purposely circumvent your face recognition system. Such a user could try to hold up a photo of another person. Maybe they even have a photo or video on their smartphone that they could hold up to the camera responsible for performing face recognition (such as in the image at the top of this post).


Learn Python AI for Image Recognition & Fraud Detection

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Combine Python & TensorFlow powers to build projects. In this course, you will learn how to code in Python, calculate linear regression with TensorFlow, and use AI for automation. Together with a professional you will perform CIFAR 10 image data and recognition and analyze credit card fraud by building practical projects. We explain everything in a straightforward teaching style that is easy to understand. Join Mammoth Interactive in this course, where we blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to credit card fraud detection What you'll learn Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram.


Deep Learning Computer Vision CNN, OpenCV, YOLO, SSD & GANs

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Use Python & Keras to do 24 Projects - Recognition of Emotions, Age, Gender, Object Detection, Segmentation, Face Aging Master Computer Vision using Deep Learning in Python. You'll be learning to use the following Deep Learning frameworks. In this course, you will discover the power of Computer Vision in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.Computer vision applications involving Deep Learning are booming! Having Machines that can'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to: Perform surgery and accurately analyze and diagnose you from medical scans.


Holistically-Nested Edge Detection with OpenCV and Deep Learning - PyImageSearch

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In this tutorial, you will learn how to apply Holistically-Nested Edge Detection (HED) with OpenCV and Deep Learning. We'll apply Holistically-Nested Edge Detection to both images and video streams, followed by comparing the results to OpenCV's standard Canny edge detector. Edge detection enables us to find the boundaries of objects in images and was one of the first applied use cases of image processing and computer vision. When it comes to edge detection with OpenCV you'll most likely utilize the Canny edge detector; however, there are a few problems with the Canny edge detector, namely: Holistically-Nested Edge Detection (HED) attempts to address the limitations of the Canny edge detector through an end-to-end deep neural network. This network accepts an RGB image as an input and then produces an edge map as an output.