Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. Age and gender, two of the key facial attributes, play a very foundational role in social interactions, making age and gender estimation from a single face image an important task in intelligent applications, such as access control, human-computer interaction, law enforcement, marketing intelligence and visual surveillance, etc. Recently I came across Quividi which is an AI software application which is used to detect age and gender of users who passes by based on online face analyses and automatically starts playing advertisements based on the targeted audience. Another example could be AgeBot which is an Android App that determines your age from your photos using facial recognition.
Today, you will find 3D printers in the most surprising places--and all over the world. Not only that, but they are often busy doing the most surprising things for the human race. If you have been following 3D printing for even the shortest amount of time, then you may have learned to continually expect the unexpected. Machine learning and data calculations are perfect examples of this as they are now being applied in 3D via a new artificial intelligence system that performs its work through bending light. AI is built on looping calculations of numbers and data that ultimately result in recognition.
Pretrained models are a wonderful source of help for people looking to learn an algorithm or try out an existing framework. Due to time restrictions or computational restraints, it's not always possible to build a model from scratch which is why pretrained models exist! You can use a pretrained model as a benchmark to either improve the existing model, or test your own model against it. The potential and possibilities are vast. In this article, we will look at various pretrained models in Keras that have applications in computer vision.
This article was posted by Xiu-Shen Wei. Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available.
In the previous article in this series, "Diving into Machine Learning" we looked at some common approaches to machine learning, which is a subset of AI that provides systems with the ability to learn from data and improve over time without being explicitly programmed. In this latest article in our Enterprise AI series, we provide an overview of deep learning, which is a specific approach to the more general category of machine learning. As with other machine learning techniques, deep learning is an important building block for artificial intelligence in the enterprise. First, let's quickly review what machine learning is. Machine learning refers to the process of training a model, which is nothing more than a function that maps inputs (e.g., house size, customer preferences) to outputs (e.g., house value, new product recommendations).