Face recognition using deep learning under 5-minutes



Original Source Here

I have embarked on a journey to learn deep learning. “For the things we have to learn before we can do them, we learn by doing them.” — Aristotle.

In the last article, we learned about face detection using MTCNN. Now, we will try to recognize the faces extracted by MTCNN. I have compiled a dataset of 30 Bollywood celebrities for our face recognition task. Every star has around ten training and ten validation images. FaceNet is a face recognition system developed in 2015 by Google researchers in a paper titled “FaceNet: A Unified Embedding for Face Recognition and Clustering.” Given a picture of a face with input dimensions (160, 160, 3), FaceNet will extract high-quality features from the face and predict a 128 element vector representation called face embeddings. Then, we will use Support-Vector Machine to classify these face embeddings into respective Bollywood celebrities.

Block diagram of our face recognition system

First, we will extract faces from all our celebrity images using MTCNN and save the extracted face pixel arrays in uncompressed .npz format. Next, make sure to split celebrity images into train and validation folders.

That’s it. The bollywood-data.npz contains a list of all faces that MTCNN detected in our celebrity dataset.

Next, we will transform the extracted faces into face embedding using FaceNet. Finally, we will save all our face embeddings for into bollywood-embeddings.npz.

After extracting face embeddings, we will build a classifier using Support Vector Machine to classify face embeddings to respective labels. We can choose any multi-class classifier, including building our Neutral Network.
I choose Support Vector Machine due to its simplicity and popularity in other machine learning blogs for the face recognition step.

We now evaluate our model using our test images. Finally, we will plot our results to have visual confirmation of SVM model performance on test images.

Plot results from test images

Tada 🎉 We have our face recognition code. How well the model performs depends on the size of our dataset. There are many other alternative models available, and we can always plug better models and experiment to see if we get better results.

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot



via WordPress https://ramseyelbasheer.io/2021/07/31/face-recognition-using-deep-learning-under-5-minutes/

Popular posts from this blog

I’m Sorry! Evernote Has A New ‘Home’ Now

Image
Original Source Here My Apologies! I was critical about the grandfather of all apps — Evernote a few months back when I wrote Why is Evernote losing its Ground? on Medium. With that said, I also wrote another story on Evernote in November of last year — Has Evernote Made A Comeback After Years Of Stagnation? This was the time when Ian Small and his team were making inroads into the technology and strategically making enhancements and revamping the app across all platforms. Evernote Is At A War-front And Wants To Prove A Point. I must h ave written this in my earlier blogs but just to re-iterate — I have been and am an avid user of Evernote since 2009 and is a part of my life for planning my daily life, projects, content writing, and almost everything. Recently, Evernote has launched its HOME or you may call it a DASHBOARD that provides its users a one-stop place to get access to their important and critical notes and also provides a few AI-enabled capabilities that I wil
Read more

Jensen Huang: Racism is one flywheel we must stop

Image
https://venturebeat.com/wp-content/uploads/2021/07/jensen-huang.jpg?w=1200&strip=all Original Source Here All the sessions from Transform 2021 are available on-demand now. Watch now. Jensen Huang , CEO of Nvidia , received the distinguished lifetime achievement award by the Asian American Engineer of the Year from the Chinese Institute of Engineers (CIE) group. And in his acceptance speech, he made a rare comment beyond Nvidia’s business matters: “Racism is one flywheel we must stop.” The comments came at the end of an acceptance speech where he thanked the group for its award, an annual gathering that recognizes outstanding Asian American scientists, engineers and role models. In a virtual ceremony, Huang was awarded for his contributions as “a visionary and innovator in parallel computing technology that accelerates the realization of AI computing.” He also spoke of his experience as an immigrant and an Asian American. “It is strange to accept a lifetime achievement
Read more

Fully Explained DBScan Clustering Algorithm with Python

Image
Original Source Here Fully Explained DBScan Clustering Algorithm with Python Unsupervised learning in machine learning on density-based clusters Clustering In unsupervised Learning. A photo by Author In this article, we will discuss the machine learning clustering-based algorithm that is the DBScan cluster. The approach in this cluster algorithm is density-based than another distance-based approach. The other cluster which is distance-based looks for closeness in data points but also misclassifies if the point belongs to another class. So, density-based clustering is suited in this kind of scenario. The cluster algorithms come in unsupervised learning in which we don’t rely on target variables to make clusters. In a cluster, the main concern is on maximum dense connected points. The density-based clustering can be useful in arbitrary shapes even with noise. The working function of the density-based cluster is shown below: We select a random data point. Then it will
Read more