Computer vision is an exciting and growing field. There are tons of interesting problems to solve! One of them is face detection: the ability of a computer to recognize that a photograph contains a human face, and tell you where it is located. In this article, you'll learn about face detection with Python. To detect any object in an image, it is necessary to understand how images are represented inside a computer, and how that objects differs visually from any other object. Once that is done, the process of scanning an image and looking for those visual cues needs to be automated and optimized. All these steps come together to form a fast and reliable computer vision algorithm.
These words send a shiver down my spine. But then again, they are the only comfort I get when I use Snapchat these days. "Why is Snapchat scaring this moron?" I don't know about you, BUT I SURE AS HELL don't enjoy sharing my bed with Casper or any other creepy ghosts that this otherworld-R.S.V.P-app has brought to my life. You see, every once in a while I'm doing my dog filter faces like a normal human being in 2017; but then… my cat stops moving and stares at the end of the room… the camera refocuses… and then: it finds an invisible Dalmatian filter standing by my side. I've moved twice already… but NO LONGER!
In the past few years, face recognition owned significant consideration and appreciated as one of the most promising applications in the field of image analysis. Face detection can consider a substantial part of face recognition operations. According to its strength to focus computational resources on the section of an image holding a face. The method of face detection in pictures is complicated because of variability present across human faces such as pose, expression, position and orientation, skin colour, the presence of glasses or facial hair, differences in camera gain, lighting conditions, and image resolution. Object detection is one of the computer technologies, which connected to the image processing and computer vision and it interacts with detecting instances of an object such as human faces, building, tree, car, etc.
In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the use of the gradient and the Laplacian of the input image together with the original input into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, the plain CNN is not able to extract this information from the raw images. We have performed a number of experiments on two well known datasets KDEF and FERplus. Our approach enhances the already high performance of state-of-the-art FER systems by 3 to 5%.
In this article, I will present several techniques for you to make your first steps towards developing an algorithm that could be used for a classic image classification problem: detecting dog breed from an image. By the end of this article, we'll have developed code that will accept any user-supplied image as input and return an estimate of the dog's breed. Also, if a human is detected, the algorithm will provide an estimate of the dog breed that is most resembling. This project was completed as part of Udacity's Machine Learning Nanodegree (GitHub repo). Convolutional neural networks (also refered to as CNN or ConvNet) are a class of deep neural networks that have seen widespread adoption in a number of computer vision and visual imagery applications.