Augmented Reality Glasses

Augmented reality (AR) technology enables a user to overlay digital content on top of the real world. It allows users to see information like a map or schematics, and interact with it organically.

Despite the hype, there have been several problems that prevented AR glasses from becoming the bestselling headset of the future. Apple has now suspended work on the product, and plans to focus on a lower-cost, physically light AR headset this year.

Positioning of face feature points

AR glasses, also known as smart glasses, use a camera and sensors to overlay digital 3D images and holograms on the user’s surroundings. These glasses may also use geolocation methods such as GPS or SLAM (algorithm-based simultaneous localization and mapping technology that also gets data from sensors) to determine the user’s location.

These glasses are usually designed for outdoor use and so feature a lens that allows them to display digital 3D images and holograms over the user’s real-world surroundings. In addition, these devices often include augmented reality navigation capabilities that allow users to navigate through their environment with hyper-personalized ad displays in their lenses.

The positioning of face feature points is a basic task in the field of computer vision that can be used to perform many visual tasks, including face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Facial key point detection has been a research focus of scholars for many years.

In recent years, a variety of facial key point detection methods have been developed. Some of them are CLM-based, active appearance model (AAM)-based, regression-based and other methods.

A constrained local model (CLM)-based method uses a shape model to fit the image to the facial features, while an AAM-based method learns a mapping function from the facial appearance to facial feature points. Among them, cascade regression models, a popular method in the early years of deep learning, is an effective model for the detecting of facial key points, as it gradually refines the coordinates of facial key points from coarse to fine.

This paper proposes a real-time face key point detection algorithm that uses the attention mechanism to enhance the multiscale characteristics of face key point features, and combines the attention module with the VGG network structure. The attention module improves the feature extraction ability of the standard VGG model, and the feature enhancement module and the feature fusion module are added to the algorithm.

The proposed algorithm can effectively realize the detection of face key points, and the recognition accuracy is better than other similar methods. Furthermore, the proposed method can solve some problems of existing high-accuracy face key point detection methods in real-time application scenarios.

Face alignment

When using AR glasses, face alignment is one of the important steps in the pipeline. The results from this step are used in the next steps, such as geometrical transformations, face normalization, segmentation or head pose estimation.

Face alignment is considered as a multi-task learning problem and is usually solved by deep convolutional networks with multiple levels to reduce drift of the results. The alignment results from each level are averaged to reduce variance and then compared with predictions from the previous level. These results are then re-averaged in the current level to refine alignment predictions.

Cascaded regression methods are a popular method for face landmark detection and tracking and show good performance in terms of accuracy, speed and computational efficiency. They progressively correct the initial shape ar glasses of facial landmarks by geometrically transforming them using the face bounding box, and then gradually regress them across different stages with a regression function learned during the training phase.

The resulting models are then verified with a set of reference images. Several evaluation metrics are available, such as Landmarks Mean Squared Displacement (laMSD), which compute how the landmarks moved along a video with a static face.

Another metric is Normalized Jitter Sensitivity Mean Square Error (NJS-MSE_s2 ), which measures the jitter robustness of a model to random variations of a face rectangle based on small center shifts in the horizontal and vertical axis. This metric is especially useful in real-time face detectors to improve the robustness of the alignment results to unpredictable changes on the position of the face.

In addition, a new metric, called Computational Efficiency, is introduced to measure the time required for inferring a face shape and the number of frames processed per second by the model. This metric excludes any other processing that is not related to the face shape inference, such as data reading and loading or pre-processing steps such as data augmentation.

These optimization and training strategies are applied to state-of-the-art face alignment algorithms with the aim of improving their operation in real time, both in embedded devices and desktop computers. The results of this work show that clever strategies can significantly increase the performance of these methods, in terms of accuracy, speed, model size or failure rate in challenging conditions.

Head pose estimation

The head pose is a very important feature of human movement and is used to understand people’s behaviors in several applications including augmented reality, driver assistance, and facial expression recognition. It also reveals valuable clinical information on motor control and its progression. However, detecting and estimating a person’s head pose is difficult for many reasons, such as extreme head movements, occlusions, and other faces in the frame.

Despite the fact that recent Deep Learning techniques have made it possible to achieve near-human quality on tasks like object detection and face landmarks estimation, head pose estimation is still one of the most challenging tasks in computer vision. Fortunately, a number of approaches have been developed to address this problem.

A common approach is to use RGB-D data for the head pose estimation task. In this method, 3D features extracted from the RGB-D data are fused with 2D features estimated in depth images to produce a dense representation of the head pose.

This dense representation is then used to estimate the head pose directly in a fine-grained manner. It involves a Mixture Block that consists of element-wise multiplication of the outputs from two streams and a Prediction Head that regresses three head pose predictions from each stream.

In this method, the weighted mean of all the predictions is used to calculate the final head pose. This algorithm is especially useful when a large amount of data is needed to achieve accurate results.

Another option is to use model-based methods that utilize ar glasses depth information from depth cameras. These methods usually result in higher estimation accuracy than image-based techniques but require more expensive hardware.

When estimating head pose, it is important to find the best algorithm for your application. This requires an evaluation of the algorithm’s robustness to different scenarios, such as (self) occlusions and other faces in the frame.

This type of analysis can be done by comparing the estimated head pose with a known ground truth. This is achieved by examining the calibration matrix and head center.

This is an effective way to test the head pose estimation method. Moreover, this method is also useful for determining the position of the eyes and nose. This helps to ensure that the augmentation with the virtual objects will be accurate.

Try-on process

AR eyeglass try-on technology is a scalable solution that can transform any brand’s entire product assortment into digitized 3D models for customers to virtually try on in seconds. These augmented reality eyeglasses are a great way to enhance customer engagement, increase sales, and improve conversion rates.

With virtual eyeglasses, customers are able to visualize how their frames will look on their face and interact with the product in a visually appealing, fun way. This enables them to feel confident in their decision-making process and ultimately make a purchase.

In addition to eyeglasses, many online stores now offer virtual try-ons for other types of products such as watches, furniture, and home accessories. These allow consumers to view realistic 3D models of their chosen products, as well as check the dimensions, material, color, and style of each item.

For example, Warby Parker uses an AR tool to give their customers the ability to virtually try on glasses before they buy. This not only helps them streamline the buyer’s journey, but it also encourages social sharing and word-of-mouth marketing.

Moreover, virtual try-on is a great way to build brand loyalty. It enables customers to experiment with different styles at their own pace and helps them choose the best fit for them. This reduces the risk of returning items and gives them a better shopping experience, which ultimately leads to increased conversion rates and reduced e-commerce returns.

Additionally, some brands have a see-now-buy-now button on their virtual try-on page that allows visitors to seamlessly transition to the shopping cart. This not only helps meet the needs of young people, it also increases purchase confidence and encourages shoppers to make a purchase.

As a result, brands can see tremendous positive results, such as +200% and +300% boosts in consumer engagement and time spent on the site. This translates to greater revenue and customer retention, which ultimately drives repeat business.

The try-on process is a great way to get new customers in the door, especially in beauty and fashion. This is because a see-now-buy-now experience is an ideal fit for young, inquisitive shoppers and enables them to test products before they purchase.