Not sure about this one… Tips to fool facial recognition systems are fast becoming obsolete. New research using partial features is showing a high success rate. The future looks bleak for privacy advocates. Facial recognition systems have exploded in popularity in the last couple of years. From filing for unemployment benefits to Uber identity checks, the technology is seeping into every aspect of our daily lives. But alongside increased security comes a dark side: invasion of privacy, unsanctioned government…… Read more...
This series of articles focuses on Deep Learning algorithms, which have been getting a lot of attention in the last few years, as many of its applications take center stage in our day-to-day life. From self-driving cars to voice assistants, face recognition or the ability to transcribe speech into text.
These applications are just the tip of the iceberg. A long path of research and incremental applications has been paved since the early 1940’s. The improvements and widespread applications we’re seeing today are the culmination of the hardware and data availability catching up with computational demands of these complex methods.
In order to fetch images from a camera device and update the Tkinter GUI, the following script can be utilized. In line 7, it uses the function VideoCapture by OpenCV, where the parameter should correspond to your device. The default camera id is usually 0, however, if it doesn’t work, you can try with 1 or -1. In case you wish to utilize video instead, you should be able to replace the device id with a video path, but there might be a few other adjustments required. In line 26, it calls the function itself again after one millisecond.
In contrary to most computer vision applications where you train a model to classify a desired class by presenting hundreds of examples of the class, with face recognition you can use deep metric learning.
Two prominent concerns emerged in the debate around the implementation of AI in fighting crimes. Authoritarian governments exploiting AI surveillance and biases in facial recognition technology.
Artificial intelligence (AI)¹ has been rapidly growing worldwide, with new applications being discovered every day. While AI has applications across many sectors, one area where it is commonly utilized is in AI surveillance and facial recognition technology to combat crimes. As of 2019, at least seventy-five countries globally are actively using AI technologies for surveillance purposes, including smart city/safe city platforms, facial recognition systems, and smart policing initiatives (Feldstein 2019: 1).
Computer Vision techniques are behind most AI applications we use daily, from the facial recognition capabilities in your smartphone to the incoming cashier-less retail stores, and let’s not forget everyone’s favourite car brand’s autonomous vehicle functionalities. It’s almost crazy to think solving Computer Vision was once a University’s student summer project back in the ’60s, or so the story goes.
Within the field of CV, there are many problems to be solved; the common ones are object detection, object recognition, pose estimation, gesture recognition, face detection, depth estimation etc. I won’t be delving into the details of common CV problems, But you can see there’s a lot to keep you busy within the field of CV.
Among other tasks made possible through machine learning algorithms, face detection and recognition is a crucial computer vision task. To begin with, both face detection and recognition are co-related yet colloquially different. Face detection is a wider aspect than face recognition and is applied with the help of machine learning. Whether it is about face detection in surveillance, mapping images for medical diagnosis, or deep analysis of human faces in videos for intelligence purposes, ML…… Read more...