In the last few decades, the research on face recognition has become a trendy topic. Significant developments have been made by various research in this field. The definition of face recognition can be expressed as a task using biometric features to recognize the human face. The tasks of face recognition can be divided into three types: Face verification (are they the same person), face identification (who is this person), and face clustering (find common people among these faces). To check what we have learned in this semester, we decided to train a face verification model in a real-world dataset with deep learning approaches. The dataset we used is the Labeled Faces in the Wild Database (LFW). The LFW database is a well-known in face recognition field. It contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person in the picture. There are 1,680 individuals have two or more distinct photos in the dataset. Our method followed the general machine learning workflow: starting with data preprocessing, building model from baseline then improving the model step by step to gain a better performance on the test data. This course aims to train a deep learning model for face verification task. Similar to other machine learning tasks, our method is a purely data driven method as the faces are represented by the pixels. The deep learning network we used is the convolutional neural network. Although face recognition model has been developed very sophisticated, our project is proposed to check the deep learning knowledge we have learnt in this semester. Thus, this course can be regard as a deep learning model training for face recognition in action. With limited computational power, we managed to adjust the model to its best performance.
Simply, click the "Book" button of Face Recognition In Action LFW and proceed to the payment method. Enter your desired schedule of training. You will receive an email confirmation for Face Recognition In Action LFW and a representative / trainer will get in touch with you.
|July 7, 2022 (Thursday)||09:30 AM - 04:30 PM|
|July 21, 2022 (Thursday)||09:30 AM - 04:30 PM|
|August 4, 2022 (Thursday)||09:30 AM - 04:30 PM|
|August 18, 2022 (Thursday)||09:30 AM - 04:30 PM|
|September 1, 2022 (Thursday)||09:30 AM - 04:30 PM|
|September 15, 2022 (Thursday)||09:30 AM - 04:30 PM|
|September 29, 2022 (Thursday)||09:30 AM - 04:30 PM|
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