Computer Vision and Its Application in Facial Recognition and Object Classification.

Author(s): Raman Rounak

Originally published on Towards AI.

Author: Rounak Raman

Introduction:

Vision is the most crucial sense in the human body, allowing us to see, assess, and analyze the environment around us. The majority of information in the world is obtained through the sense of sight. As humans, we have the ability to distinguish and recognize different patterns, including distinct facial features. With the advent of the computer age, scientists and tech companies have been striving to implement this visual capability in computer machines, leading to the birth of the field of computer vision. Thus it is rightly said by a famous English writer:

“Where words are restrained the eyes often talk a great deal”

Problem Statement:

“Just like to hear is not same as to listen, to take pictures is not same as to see”.

The mere act of capturing images and analyzing them picture by picture does not encompass the fundamental concept of computer vision. Computer vision aims to develop algorithms and techniques that enable computers to understand and interpret visual data in a manner similar to human vision.

Early Developments:

Viola-Jones Algorithm:

The Viola-Jones algorithm, introduced in 2001, revolutionized face detection by combining Haar-like features and the AdaBoost learning algorithm. It provided a significant improvement in speed and accuracy for face detection tasks. There always has been a trade-off between accuracy and speed in terms of face detection. If we feed more features to be analyzed in terms of object recognition, the speed gets affected, and if we increase the speed by which the images are processed, the resolution gets affected.

AdaBoost Learning Algorithm:

This supervised algorithm basically combines the predictive power of different machine learning algorithms like decision trees, KNN, Logistic Regression, etc., on the same dataset to generate output with high accuracy. In the case of the decision trees algorithm, it first assigns equal weights to all data points and then measures the incorrect predictions, it then assigns heavy weights to those data points, and the weights of the correct prediction data points are decreased so that the next time we run the model, those particular data points are given more importance. Hence AdaBoost is a classifier just like gradient descent to improve the accuracy of the model.

Convolution and Deep Learning Algorithms:

Convolutional Neural Networks (CNNs) emerged as a groundbreaking technique for computer vision tasks. CNNs leverage convolutional layers to extract features from input images and enable accurate object classification. Here deep neural networks are used to build a model in order to detect different types of objects. Each layer added to the neural network extract different features and then convolute with other feature to accurately predict the objects or faces.

Pipeline for the Development of Computer Vision Algorithms:

1. Data Acquisition:

Collecting a diverse dataset of labeled images is crucial for training computer vision algorithms. This includes an unbiased collection of images from either already present datasets or generating new ones.

2. Preprocessing:

Applying image preprocessing techniques such as resizing, normalization, and noise reduction to enhance the quality of input images. This also includes augmenting the images in order to increase the size of the sample or training dataset.

3. Feature Extraction:

Extracting relevant features from images using techniques such as edge detection, color histograms, and texture analysis.

4. Model Training:

Utilizing machine learning algorithms like Viola-Jones or CNNs, to train the model on the labeled dataset.

5. Model Evaluation:

Assessing the performance of the trained model using metrics like accuracy, precision, and recall.Also, plotting the performance of the model.

F.R.I.D.A.Y-Facial Recognition Image Detection and Analysis System

This was the project I developed following the above steps I mentioned in this article as well as researching the topic. A brief summary of the same In the F.R.I.D.A.Y project, I created an application focused on facial recognition, image detection, and analysis. The main objective was to develop a reliable system capable of real-time face detection and analysis using computer vision techniques. The project involved building a face detection model and implementing an object classification pipeline. To train the face detection model, I collected images using a webcam and used an augmentation library to increase the dataset size by randomizing the images through adjustments in brightness, gamma value, and cropping. This resulted in a larger and more diverse dataset for training. Bounding boxes were used to annotate the faces in the images, providing ground truth information for the model. The face detection model consisted of a classification model to determine the presence of a face and a regression model to draw a bounding box around the face by estimating the coordinates. Binary Entropy Loss was used for the classification model, while Mean Squared Error (MSE) loss or localization loss was employed for the regression model. The neural network model was built using the Keras API, specifically utilizing the VGG16 model pre-trained on a vast image dataset. Additional layers were added to the VGG16 model for classification and regression. The trained model produced five values as output, including a probability value for classification and four coordinates for the bounding box. Overall, the F.R.I.D.A.Y project successfully developed a robust system for real-time facial recognition and image analysis, integrating various computer vision techniques, data augmentation, accurate annotation, and well-defined loss functions to achieve accurate face detection and localization. The code for the same is given below:

Recent Developments and Research:

Highlight recent advancements in computer vision, such as a. State-of-the-art object detection algorithms like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector).

Advancements in facial recognition techniques, including the use of deep learning models like FaceNet and ArcFace.

Real-time video analysis and tracking using optical flow and motion detection techniques.

Assumptions and Challenges Faced in Facial Recognition and Object Classification:

Discuss the assumptions made and challenges encountered in the development and implementation of computer vision algorithms for facial recognition and object classification tasks. This may include issues related to lighting conditions, occlusion, pose variation, and the need for large labeled datasets.

In recent years the AI generative image model has been facing issues in generating an accurate image of hands. This is because, unlike of face, the hands include fingers and palms, which can be depicted in various different shapes and combinations, and there is still not enough data for the model to be trained on. This highlights the issue of how sufficient data should be present in order for the model to be accurate.

This situation is well depicted and documented by Vox in one of its videos:

Future Prospects and Applications:

Explore the potential applications of computer vision in various fields, such as surveillance systems, autonomous vehicles, augmented reality, medical imaging, and robotics. Discuss future prospects, including the integration of computer vision with other emerging technologies like artificial intelligence and the Internet of Things. Its use is dependent on the user. E.g., on the one hand, we are seeing the rise of GPT-4, which is bringing a revolution in the access to information and its accuracy, and the recent launch of Vision Pro glasses by Apple Inc. On the other hand, we can see the use of Computer Vision being used to regulate and make a dystopian society in China with little to no privacy by using the Social Credit System. There is a detailed video you can see below:

Source Citation and References Used:

https://iopscience.iop.org/article/10.1088/1742-6596/1755/1/012006/pdf

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