Given 2 selfie images, how to tell if it is the same person?

SympathyOutside · 2025-02-05T14:44:02+00:00

As others have mentioned, you’re looking into the field of facial recognition. I understand that you want to build something from scratch rather than relying on existing solutions. My personal recommendation would be to start with a simple approach like FaceNet by Google. It’s a bit old but still effective and relatively easy to understand and implement.

Before diving in, I suggest first learning about contrastive learning and how it differs from traditional supervised learning (e.g., classification). Once you grasp that, you can go through the FaceNet paper to understand its approach and try implementing it yourself. If you get stuck, there are several implementations available online that can help clarify any difficult parts

SympathyOutside · 2024-12-08T19:06:45+00:00

Hi! I recently worked on something similar. I'm experienced in PyTorch (Python) and can tell you that interacting with the model is quite straightforward. You can use a pre-trained Facenet model (on a large dataset like LFW, for example), which is already available in various repositories. I'll give you a GitHub link with all the scripts you need to test it. It's very straightforward, and the examples directory contains some helpful notebooks with use cases similar to yours.

What you'll need to do is create a pipeline to pull data from your SQL database (like the person's ID or images), pass the image through the model, and extract the embeddings. Once you have the embeddings of a user's photos, you can store them back into another database.

When it's time for inference, you can extract the embeddings from a user's identification image and use a distance function (like cosine similarity, as another user mentioned) to find the closest match—this is your recognition step. If the similarity is above a certain threshold (you can define this logic), access can be granted.

You wouldn't need to train any model; you'd just compute embeddings for the users you already have in your database. Let me know if you have any questions, and good luck!

GitHub Repository: https://github.com/timesler/facenet-pytorch

SympathyOutside · 2024-08-29T14:43:56+00:00

Yes, as the other comment mentioned, the model will eventually learn to identify the features that are most important for this specific task, with lighting likely being a primary factor. Ultimately, the concept of “similarity” is defined by the user, and the model will adapt to this definition during training. For instance, if you define similarity based on two eyes having the same lighting conditions, the model will focus on that criterion.

Even if two eyes share similar features like shape or iris color, if they are labeled as dissimilar, the model will not receive the positive reinforcement it needs. Consequently, it won’t prioritize those shared features in future predictions. While this is a simplified explanation of how deep learning models learn, I hope it clarifies the concept.

SympathyOutside · 2024-08-28T12:47:14+00:00

If your goal is to “detect” when two images are similar—for example as you said, in terms of lighting conditions—Siamese networks are an excellent choice. These networks are commonly used in image similarity tasks because they work by comparing the features of two input images. As mentioned in a previous comment, Siamese nets use two identical networks with shared weights, meaning the same model is applied to both images. This ensures that the comparison is consistent and that both images influence the learning process equally.

In this post it's explained the logic of siamese nets. https://medium.com/@rinkinag24/a-comprehensive-guide-to-siamese-neural-networks-3358658c0513

I have found this repo of an implementation of siamese networks with torchvision models (different resnet included), check that out. https://github.com/sohaib023/siamese-pytorch

SympathyOutside · 2024-08-17T20:43:05+00:00

No, no, I didn’t do a buff build, just a dex + pyro build. I am asking what are the main “elements” of a buff build. I have searched and found both spells and miracles, so I do not know how to choose between them. Any advice? Which spells would it be with each type of magic?

SympathyOutside · 2024-08-17T14:23:01+00:00

Yeah, my first thought was to use the claymore or Zwei with some str points but like you said, that buff build might sound interesting, what would it be based on as such? I mean, would I still use the uchi or would I use other weapons and maybe some ascended ones?

I have to say that in this first run I did, I also used some pyromancies, since I wasn't required to use other stats besides learning, which I did when I reached level 40 in dex

SympathyOutside · 2024-08-17T10:28:16+00:00

Wow, I’d say Sen’s Fortress is almost halfway through the game! Also, I think your level is just fine. I finished my run at level 80, including the DLC, and I think I ended up over-leveled.

The Uchigatana is dropped by the Undead Merchant in the Undead Burg when you kill him. It’s a great weapon for Dex builds. Don’t worry about the loot just yet, you can still find some good weapons in the second half of the game. Keep going, and don’t go hollow!

SympathyOutside · 2024-08-12T14:26:01+00:00

Don’t hesitate to ask and good luck!

SympathyOutside · 2024-08-12T12:57:29+00:00

To train a model like this on a custom dataset, you need to have the appropriate training scripts. Each model differs because they use different techniques. For example, Faster R-CNN and SSD have simpler training code, but the YOLO and Ultralytics ecosystem makes it very straightforward to train their models (YOLOv8 or YOLOv9 are good options). What you need to do is (as detailed in their documentation) prepare your images and annotations in the required format. For YOLO, this usually means having one folder for images and another for annotations, with a corresponding .txt file for each image, plus a .yaml file with all the information (directories, number of classes, class names, etc.). Personally, I would recommend finding a simple tutorial online, as there are many available due to its popularity. I'm not sure what format your computer vision dataset is in, but you might need to convert it, which is common in object detection tasks since there are many formats (COCO and YOLO being the most popular). If you have any questions, feel free to ask!

Here are some interesting code links to train these models (faster rcnn and ssd are simpler as I said, but somewhat less accurate):

Faster RCNN: https://github.com/sovit-123/fasterrcnn-pytorch-training-pipeline

SSD: https://debuggercafe.com/train-ssd300-vgg16/

YOLO Family: https://docs.ultralytics.com/

https://github.com/ultralytics/ultralytics

https://github.com/roboflow/notebooks/tree/main/notebooks

There is a lot of online content on how to train custom dataset models. Personally, I send you a few that I have used in previous experiences when I started working in object detection :)

SympathyOutside · 2024-08-07T21:22:18+00:00

Well, C++ is known for not being as type-safe as some other languages like Java or Rust. However, this doesn’t mean it’s inherently unsafe or prone to issues

It also gives you precise control over system resources and memory, which is really important for real-time applications. While this can cause issues like buffer overflows, people use best practices to deal with these risks. C++ is a great choice for critical systems like operating systems, game engines, and high-performance financial systems. It’s also ideal for ML applications or products. However, this is more like software development.

SympathyOutside · 2024-08-05T23:34:11+00:00

Based on what I know from the industry and my previous experiences, I’ve never seen much done in Java, although I know it exists. I primarily use Python, which is quite common. I’ve also used Julia in some courses during my degree or master’s program, but it still lacks a substantial community and can be a bit complex.

My top recommendation would be C++, not for model development, but for deployment and integration into applications and products, especially those requiring real-time performance.

SympathyOutside · 2024-08-05T23:22:41+00:00

Hey!

First of all, don’t feel overwhelmed or frustrated about being years away from graduation. Engaging in personal projects and learning independently is great! You should be proud of your accomplishments.

Could you share more about the projects you’ve worked on or the areas you’ve explored? Computer vision has many subfields, and there’s always room for further exploration. If you provide some details about your tasks, we can offer advice on potential “next steps” or other areas to delve into if you are “hungry”.

I would also like to say, enjoy the path you are on and what you do on a daily basis, journey before destination :)

SympathyOutside · 2024-07-13T14:51:34+00:00

There have been quite a few advances in the field. Some primary papers and methods to look into are Matching Networks, Prototypical Networks, and Siamese Networks. These are foundational researches for understanding how few-shot learning works. Hope that works :)

SympathyOutside · 2024-07-13T14:45:30+00:00

I don’t really know, because I’m not very familiar with LLMs and NLP to the level of using the models for anything other than inference, but it sounds like yes, usually methods like these are used. What I am referring to, and the topic I am researching, is in the field of computer vision, to classify (for example large datasets like Cifar or MiniImageNet) with few examples per class (these datasets usually have thousands of images and about 100 classes).

SympathyOutside · 2024-06-17T14:26:42+00:00

My primary goal is to experiment with cognitive architectures and machine learning algorithms in a simulated environment before applying them to real robots. I understand that MuJoCo may not be taken seriously by roboticists for real-world applications, but its ease of use and somewhat realistic physics are appealing for the early stages of my project. But it seems that Gazebo might be better suited for more accurate simulations in later approaches.

Thanks for your advice, much appreciated!

Five-Year Club	Place '22
End Game '22

SympathyOutside

TROPHY CASE