In the city of bright lights and sleek rides, a new era unfolded for automobile owners. The vehicles could recognize their owners at a glance and come alive with a futuristic hum. Keys were yesterday; cars in this city identified their owners like old friends with the help of face recognition technology.
In a world where automotive security troubles ranged from the annoying to the dangerous, this technology was a game-changer. No more fumbling for keys—security was as simple as looking at your car and feeling it respond.
As the owners settled into their seats, a sense of trust enveloped them. The car became an extension of them, a smart companion navigating the bustling streets. In a landscape of evolving threats, facial recognition stood as a silent guardian, ensuring the driver and the vehicle were in perfect sync. This is not just a drive but a seamless experience that blends technology and security.
Automotive technology enthusiasts know that this is not just a vision, it’s something we can witness in our cars in the future.
In the current scenario, Automotive OEMs are slowly, but surely, beginning to trust and integrate face recognition as a reliable and modern security feature. Exhibit A – Hyundai’s Genesis GV60.
The growing trend and trust in intelligent solutions set a precedent for such technology to thrive! This article breaks down the vehicular biometric technology in facial recognition.
The Need for Advanced Automotive Safety and Security
Technology has been evolving at lightning speed, but so are the threats associated with its secure usage. Automobile theft poses a real problem, with 1,001,967 stolen in the US during 2022 alone. Thus, integrating advanced security solutions to protect our prized assets on wheels is the need of the hour.
Face recognition in cars not only helps secure the owners from preventing theft, but it can also ensure driver safety. For instance, the camera in any face recognition system plays a crucial role. With its inputs and tailor-made algorithms, the vehicle can assess the driver’s fatigue and offer necessary safety alerts and suggestions.
The alerts can be delivered through seat vibrations and alarms, while the infotainment system and rear seat entertainment setup can provide suggestions to the driver and passengers if they opt for it.
The Origins
First things first, face recognition technology isn’t new, but integrating it as a car safety feature sure is. Woodrow M Bledsoc and his team were the first to experiment with this futuristic solution between 1964 and 1966. The objective of the experiments was to look into the possibility of recognizing faces through computer programming.
However, due to the novelty at the time, the team faced several challenges to overcome the variability recognized by the computer. Appropriate solutions (algorithms) to nullify the challenges are suggested below:
| Challenges reducing the efficiency of Face Recognition | Algorithms to nullify the challenges of Face Recognition |
| Head Rotation | Multi-Angle Detection |
| Tilting | 3D Face Recognition |
| Light intensity and Angles | Lighting or Illumination Normalization |
| Facial Expression and Aging | Dynamic Feature Analysis and Independent Component Analysis |
Despite achieving accuracy up to 99.97%, some of these problems still haunt the accuracy of facial recognition.
Recognizing Owners by their Facial Features – The Process
A conventional face recognition system typically entails the sequential processes of face detection, feature extraction, and processing.
Face recognition involves comparing an image against a stored repository of faces, aiming to ascertain the identity of the subject portrayed in the input image. Several pivotal factors, including shape, size, pose, occlusion, and illumination, intricately influence this identification process.
Primary face recognition focuses on discerning unique facial landmarks, including nose width, eye dimensions, jaw attributes, cheekbone elevation, and eye separation. This leads to the creation of a distinctive numerical code. Subsequently, the system undertakes a comparative analysis of this numerical code with another image, discerning the degree of similarity between the two pictures.
Automobiles such as cars follow a similar procedure to recognize faces!
Algorithms that Enable Face Detection and Recognition in Automobiles
The development of AI & ML algorithms is the heart of face recognition technology in cars. They leverage advanced mathematical and computational techniques to extract meaningful features, learn patterns, and accurately predict facial recognition tasks.
While designing technology of this magnitude for automobiles, the following algorithms have resulted in highly accurate solutions:
Eigenface-based methods (PCA algorithm)
Principal Component Analysis (PCA) identifies patterns by transforming correlated variables (pixel values in face images) into a new set of uncorrelated variables called principal components. Eigenfaces are the principal components representing the most significant variations among face images.
The car uses these eigenfaces for recognizing and categorizing new faces, focusing on the features that contribute the most to the overall variability. Principal Component Analysis aims to decrease the complexity of the data by maintaining as much of the original dataset’s variation as feasible.
Linear Discriminant Analysis (LDA)
LDA aims to find a linear combination of features that maximizes the separation between different classes (individual faces). It calculates the between-class (quantifies the variability of data points belonging to the same facial feature) and within-class (quantifies the separation between different facial features) scatter matrices to determine the optimal projection that maximizes differences between the faces of different people while minimizing variations within the same person’s face.
This results in a set of features that effectively differentiates between individuals.
Independent Component Analysis (ICA)
ICA assumes that a face image is a linear combination of independent sources, each representing a specific facial feature. After breaking down the facial features into independent components, the algorithm isolates and identifies individual features. This robust approach allows the model to handle variations in lighting, expressions, and other factors.
Elastic Bunch Graph Matching Technique
This technique represents faces as interconnected nodes connected by elastic bands, forming a graph that captures the spatial relationships between facial features. The elasticity enables the model to adapt to variations in facial expressions or poses, providing a flexible and accurate representation to recognize faces in cars.
Neural networks (NN)
Neural networks consist of layers of interconnected nodes (neurons), each associated with weights that get adjusted during training. These networks learn hierarchical representations of features, capturing complex patterns in face images.
The activation of neurons in the output layer corresponds to the face classification, allowing the model to generalize well to new, unseen faces.
Support Vector Machine (SVM)
Support vector machines (SVM) classify faces by finding the hyperplane that maximally separates different face classes. Think of it like drawing a line between two groups of people in a room based on certain features, like height and hair color. The SVM is the brain deciding the best line (hyperplane) to differentiate the two groups.
So, when new faces come in, it can quickly assign the group they belong to based on their features. The system can create two groups, authorized and unauthorized to grant vehicle access! SVMs are particularly powerful when dealing with high-dimensional feature spaces.
Due to the increased emphasis on developing intelligent technologies, innovators in the automotive industry rely heavily on deep learning and artificial intelligence-based solutions.
Deep Learning and Artificial Intelligence for Face Recognition
Deep learning involves utilizing deep neural networks with multiple layers. It learns intricate features and representations from the dataset. Deep learning for facial recognition involves Convolutional Neural Networks (CNNs) or other specialized architectures. CNNs excel at capturing spatial hierarchies, making them well-suited for image-based tasks like face recognition. Feature extraction and learning are the two other essential aspects of facial recognition, and these components are utilized in making cars safer.
- Hierarchical Feature Extraction
- Deep networks learn features step by step.
- Early layers focus on basics like edges and textures.
- Deeper layers handle complex structures like facial contours.
- Representation Learning
- Deep learning is best at finding meaningful patterns.
- By encoding faces into a space where similar faces are close, deep learning assists in identifying faces under different constraints.
- It can differentiates between individuals easily.
Through continuous learning and exposure to diverse example, the AI based system’s ability to recognize faces in cars will improve over time. Custom artificial intelligence and machine learning algorithms can help automotive OEMs to unlock a wide range of face recognition-based solutions.
Conclusion
The journey towards widespread adoption of face recognition in cars is just the beginning. IoT solutions are the backbone, connecting the intricate network of sensors, cameras, and processing units. As face recognition technology evolves, the integration of IoT enables real-time communication and continuous improvement.
The synergy between face recognition and IoT empowers vehicles not only to recognize their owners but also to adapt and respond to dynamic safety challenges.
IoT service providers, such as Embitel, armed with expertise in AI and ML services, play a crucial role in shaping the future of automotive security. Our ability to develop and implement advanced algorithms will assist in harnessing the power of AI in the automotive context.
