R1: 3 out of 5!

3 out of 5, that's the score given by The Photographer to Canon R1, a camera that was or is supposed to be Canon's flag ship. 

While the camera certainly delivered on knocking wildlife, specifically birding, out of the park, the biggest gripes we’ve had with this camera and other Canon cameras is their inability to recognize people of color. This, along with the lowered megapixel count and price, is why we’re giving the Canon EOS R1 three out of five stars.

The Canon R1 has faced criticism from many experts in the field. The main issue, in my view, is that Canon already had an excellent camera—the Canon R3. Compared to the R3, the R1 feels more like an incremental upgrade rather than a truly new model. 

According to The Photographer Review, the R1 is an excellent choice for wildlife and bird photography. However, I doubt that this market is willing to pay the nearly $6,300 price tag. Sports photographers are more likely to justify such an investment. Other fields, such as event photography, can achieve great results with more affordable cameras—including Canon's lower-cost options.

 Camera autofocus systems primarily rely on contrast detection, phase detection, or a combination of both to achieve sharp focus. In low-light conditions, these systems face significant challenges, particularly when photographing people with darker skin tones. Here’s why:

Contrast Detection and Low-Light Challenges

Contrast-detection autofocus (CDAF) works by analyzing the difference in light intensity between adjacent pixels. It continuously adjusts the focus until it finds the point of highest contrast. However, in low-light environments, overall contrast is reduced, making it harder for the system to detect clear edges and fine details.

For individuals with darker skin tones, the challenge is even greater because:

• Lower Reflectance – Darker skin absorbs more light compared to lighter skin, which reflects more. This means that in dim lighting, a camera might struggle to differentiate between the subject and the background due to reduced contrast.    
• Less Edge Definition – Autofocus systems look for contrast between light and dark areas. If a person's skin tone is closer in brightness to the surrounding environment, the system may have difficulty locking focus accurately.

 Phase-detection autofocus (PDAF), which is common in DSLRs and high-end mirrorless cameras, works by splitting incoming light into two images and aligning them. While it is generally faster than CDAF, it still struggles in low-light conditions where there is minimal light to work with.

For subjects with darker skin in dim environments, PDAF may also have difficulty due to:

• Lack of Strong Highlights – Many phase-detection systems are optimized for high-contrast areas such as eyes, sharp edges, or bright surfaces. If the available lighting is insufficient to create highlights on the skin, the system may misfocus or hunt for focus longer.
• Artificial Light Sources – Some cameras compensate for low light by using infrared or other artificial light sources. However, these systems can be less effective for darker skin tones, as some infrared sensors may not reflect as strongly from melanin-rich skin.

So, what are the solutions to this issue?  Camera manufacturers have made significant progress in improving autofocus accuracy across different skin tones, particularly with advancements in AI-based subject recognition and machine-learning autofocus algorithms. Some potential solutions include:

• Face and Eye Detection Algorithms – Modern cameras now use AI-trained models to recognize facial features rather than just relying on contrast. This helps lock focus on subjects regardless of lighting conditions.
• Better Low-Light Sensors – Cameras with backside-illuminated (BSI) sensors and higher ISO capabilities perform better in dim environments, reducing autofocus struggles.
• On-Camera Lighting Assistance – Some cameras feature AF assist beams or infrared illuminators to help focus in dark settings.

The challenge is that any solution relying on improved AI-based algorithms will require more processing power, which generates additional heat. This, in turn, increases costs due to the high price of processors and the need for effective heat dissipation solutions—all while maintaining a camera size that remains appealing to the market.

As you can see, this is no easy task.

Going back to The Photographer review, they illustrate how the camera focus fails using this picture. 

As you can see, this is an extreme scenario for me—a dark-skinned person in front of a dark background. The lack of contrast is evident, and in this situation, not just Canon's cameras will struggle; most cameras would face difficulties.