Cartoon Face

Do you want to send a Pixar-like or cartoonified version of yourself to someone else on Snapchat? The adorably oversized eyes and small button-like noses are difficult to ignore and easy to share. With Lenses, you can transform your face into a variety of things, like Anime-styled art or even a 3D cartoon. This wikiHow article teaches you how to send a Snap with the cartoon face lens on Snapchat.

I am trying to implement a simple face detection of cartoons code using opencv and python. Though the code I have used works for faces of humans I am not able to detect cartoon faces using this. Is there any way I can make it detect cartoon faces too.

Cartoon Face

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Actually I agree with all above comments. There is no ready sollution for catroon objects and I've tried a lot of models and nothing works (ORB, Haar Cascade, SuperPixel and etc.) and no AutoML solution (from Google or Amazon) also does not work. There is only one way to do the face recognition in cartoons objects. This solution is firstly to make your own labeled data set of images and then try to train some models to detect objects. So I've chosen this way, and results are great, 91% accuracy.

In this image, I UV unwrapped the sphere, added the facial features as textures, and as you can see the textures bend to the shape of the face. Which is all wrong. I want to make the second face look like the first one.

Individuals with Internet gaming disorder (IGD) show deficits in face processing due to long-term Internet-game social activities based on cartoon faces in the popular online game "Strike of Kings." However, the abnormal neurocognitive mechanism of face recognition and processing in individuals with IGD has not been systematically explored. This study used event-related potential (ERP) methods and the reversed deviant-standard oddball paradigm to comprehensively compare four ERP components, namely, P100, N170, P200, and mismatch negativity (MMN), induced in the unconscious and automatic processing of realistic and cartoon faces in individuals with IGD. Results showed that, with respect to cartoon faces, individuals with IGD exhibited not only P100, P200 and MMN enhancements but also the absence of the N170 dominance effect in the left hemisphere. Our results also demonstrated that individuals with IGD had the advantages of early automatic perception of cartoon faces and automatic detection of changes in "cartoon" features. This study enhances our understanding of the mechanism of IGD from the neurocognitive perspective and provides candidate electrophysiological indicators for the clinical diagnosis of IGD.

Figure 2 Grand-averaged P100, N170, and P200 waveforms for cartoon and realistic faces in the IGD and control groups at the PO5 and PO6 electrodes. IGD, Internet gaming disorder group; Control, control group; Standard, standard stimuli; Deviant, deviant stimuli; Realistic, realistic faces; Cartoon, cartoon faces.

Figure 4 (A) Grand-averaged MMN waveforms for cartoon and realistic faces in the IGD and control groups at the PO5 and PO6 electrodes. (B) Two-dimensional topographic distribution of MMN for cartoon and realistic faces in the IGD and control groups. IGD, Internet gaming disorder group; Control, control group; Realistic, realistic faces; Cartoon, cartoon faces.

Have you watched some Twitter or TikTok videos with a cartoon face lens? These videos get so much attention and likes. Why do people love these videos? The only reason is the cartoon face lens released by Snapchat.

Snapchat is an instant messaging app. You can use it to take photos and videos and share them with your friends. People who love TikTok and Instagram also like Snapchat. It introduced a large number of funny filters, virtual avatars, and face lenses.

Snapchat began to release a cartoon lens for Disney in 2020. The lens can be put on your face to make you look like a Disney prince or princess. It bigger your eyes, smaller your face and smooths your skin. The adorable snap with a cartoon face lens always gets the best response on Facebook and Instagram.

If you want to use a cartoon virtual background for your snaps. Click the + Add Background button and select the Local Media option to upload the Disney cartoon backgrounds in your local file.

In this post, we give you a freshly updated guide on how to send a snap with the cartoon face lens on Snapchat. And if you want to get rid of the annoying background of your snap, there is a great webcam background remover. FineCam will give you the best video quality with the excellent library.

Imagine if every time you looked at a face, one side of the face always appeared distorted and cartoonish, resembling a painting by Salvador Dal. This is the case for people who have a rare condition known as hemi-prosopometamophosia (hemi-PMO), which makes looking at faces discomforting. According to a new study published in Current Biology, some people with hemi-PMO see distortions to the same half of a person's face regardless of how the face is viewed. The results demonstrate that our visual system standardizes all the faces we perceive using the same process so they can be better compared to faces we have seen before.

"Every time we see a face, the brain adjusts our representation of that face so its size, viewpoint, and orientation is matched to faces stored in memory, just like computer face recognition systems such as those used by Facebook and Google," explains co-author Brad Duchaine, a professor of psychological and brain sciences and the principal investigator of the Social Perception Lab at Dartmouth College. "By aligning the perceived face with faces stored in memory, it's much easier for us to determine whether the face is one we've seen before," he added.

Hemi-PMO is a rare disorder that may occur after brain damage. When a person with this condition looks at a face, facial features on one side of the face appear distorted. The existence of hemi-PMO suggests the two halves of the face are processed separately. The condition usually dissipates over time, which makes it difficult to study. As a result, little is known about the condition or what it reveals about how human face processing normally works.

The current study focused on a right-handed man in his early sixties ("Patient A.D.") with hemi-PMO whose symptoms have persisted for years. Like many with this condition, his distortions were caused by damage to a fiber bundle called the splenium that connects visual areas in the left hemisphere and right hemisphere of his brain. Five years ago while A.D. was watching television, he noticed that the right halves of people's faces looked like they had melted. Yet, the left sides of their faces looked normal. He looked in the mirror at his own face and noticed that the right side of his reflection was also distorted. In contrast, A.D. sees no distortions in other body parts or objects.

The study involved two experiments. In the first, A.D. was presented with images of human faces and non-face images such as objects, houses and cars, and asked to report on distortions. For 17 of the 20 faces, he saw distortions. The distortions were always on the right side of the face and facial features usually appeared to drooped. For example, in one of the faces, A.D. reported that the right eye looked a lot bigger than the left eye while the right eyebrow, right side of the nose, and right side of the lips all hung down unnaturally. Two of the face photographs that did not elicit a distortion showed right profile views in which the right side of the face was not visible. Consistent with his daily experiences, A.D. did not see distortions in any of the non-face images. These results show that his condition affects brain processes specialized for faces.

For the second part of the study, A.D. reported on distortions that he saw in 15 different faces that were presented in a variety of ways: in the left and right visual field, at different in-depth rotations, and at four picture plane rotations-- 0 degrees or upright, 90 degrees, 180 degrees or upside down, and 270 degrees. Regardless of how the faces were presented, A.D. continued to report that the distortions affected the same facial features. For example, even when a face was presented upside down, A.D. still saw the facial features distorted on the right side of the face even though the distortion now appeared on the left-hand side of the stimulus (the red-shaded region in the faces in the reproduction of Figure 4E below). The consistency of the location of A.D.'s distortion demonstrates that faces, regardless of viewpoint or orientation, are aligned to the same template similar to what computer face recognition systems do. In A.D.'s case, the output from that process is disrupted as it is passed from one brain hemisphere to the other due to his splenium lesion.

Reference: Almeida, J., Freixo, A., Tbuas-Pereira, M., Herald, S. B., Valrio, D., Schu, G., Duro, D., Cunha, G., Bukhari, Q., Duchaine, B., & Santana, I. (2020). Face-Specific Perceptual Distortions Reveal A View- and Orientation-Independent Face Template. Current Biology, 0(0).

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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