but I always get that "faces" are null reference. I followed this tutorial ( -detection-and-recognition-in-c-using-emgucv-3-0-opencv-wrapper-part-1/) but I can't make imagebox and other stuff, due to using wpf and not windows forms.

I think that the best way to do this is using emguCV (openCV for c#). I know that for face recognition I need to first detect my face, than save it somewhere in a file and only then can I compere the current face on the camera to all the saved faces in my "folder of faces".

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It would be really awesome if there already existed some sort of functions to only call them, because I already have 600 lines of code of kinect stuff (like body tracking and gesture and voice detection and voice commands, etc...)

Depending on the attachment, the device will switch to a tailored programme for the relevant body or face area. There are five settings and a sensor to identify the most comfortable one for your skin tone. You can also download a free app to create a tailored treatment schedule for each area and get advice and tips.

Depending on the attachment, the device will switch to a tailored programme for the relevant body or face area. There are also five settings and a sensor that identifies the most comfortable one for your skin tone. Plus, it comes with a handy precision attachment for smaller areas.

It comes with a number of interchangeable heads (and a Venus razor as an added bonus), which our panel said made it easier to reach most areas of the body. Our testers found it covered a lot of skin within a short space of time too, delivering on its claims, and was easy to set up and control due to its single control button.

This device claims to perform a full body treatment in less than 20 minutes, aiming to reduce hair growth and prevent stubble and ingrown hairs. With a built-in skin sensor, it checks skin compatibility too, to ensure it emits the correct light pulse. You can also connect it to an app via Bluetooth, which offers a treatment plan and a light pulse counter.

Taking advantage of this idea, we can still use woman in the first few steps and only swap in a celebrity name later on. This keeps the composition while offering to blend a generic face with a celebrity.

The main body is formed from empty recycled water bottles. Four water bottles form the abdominal area with a fifth water bottle hot glued to the top creating the neck. The fifth bottle is angled as shown in the photographs. Hot glue and masking tape are used to keep the bottles in the desired position.

The starting point for the head and face was an inflated balloon coated with papier mache strips. The head was hot glued to the end of the fifth water bottle although in hindsight it would have been easier to create the gargoyle head separately then attach it to the neck along with the wings, legs and arms.

Two tubes created from rolled up newspaper were inserted and glued into the back two water bottles that formed the body. These tubes will become the supports for the wings which will be created separately. A tail was also attached to the rear of the body, the tail was simply a shape cut from corrugated cardboard.

Once the base, body and facial armature were complete the entire form was coated with several layers of strip mache and allowed to dry. This will now act as a solid form allowing you to add the wings, arms, legs and claws.

Coconut oil has nothing to do wid complexion. Its in d genes

....if ur baby was born fair den her natural color is dat n d baby will regain it after sometime....though keeping baby in sunlight wil make d complexion dark due to more production of melamine pigment in d body

A solver pivot warning or error has been detected in the UY degree of freedom of node 111607 located in Surface Body. This is usually a result of an ill conditioned matrix possibly due to unreasonable material properties, an under constrained model, or contact related issues. Check results carefully. You may select the offending object and/or geometry via RMB on this warning in the Messages window..

I think i've found the problem. There is gap and penetration in my 2 face's bonded connection. I've tried manually change the parameter in pinball radius but still it doesn't fix the gap. How I can solve this problem?

thanks for the advice mr jj77. I've tried the line body first before in the shell mode. But there is some length limitation in the line body so the mode can't suit my model. My actual model is 6200 ft on length even shell mode can't accomodate it so i decided to break up tubing into 4 part in the different file

Dave, you have a fixed support around the entire circular edge that is part of a face that has a pressure applied. That means the face moves due to the pressure but the edge can't move and that causes the weird stress.

Use symmetry to resolve this issue. You already sliced your pipe in half. Slice it in half again to get a quarter pipe. Apply a Symmetry condition to the two slicing planes which will remove 3/4 of the pipe and you will only mesh 1/4 of the pipe circumference. Now delete the Fixed Support and use a Z=0 displacement condition. The two symmetry planes will control X and Y motion, while allowing to top edge of the pipe to expand with pressure unconstrained like the rest of the face and there will be no weird stress.

Thank you mr jj & Mr peteroznewman, I'll try using line body once more. And my last question I think

1. How I can split several line body and make it as multipart because max line body I only can make 1550 on length

Thank you mr jj, I can modelize the 2 function pressure load with split the line body into 2 parts. And my final problem now for line body method is still a solver pivot warning. Here I attach the model. I've tried any possibilities with the boundary condition and it doesnt fix anything.

Testers saw a noticeable reduction in hair here in just four weeks, and the included Facial Hair Remover enabled easy prep before using the device. Also included are four attachments with different sized windows for use on different areas of the body, including the face and bikini line.

With 10 intensity levels to choose from, this device offers the most personalised experience of all the models we tested. Our panel liked the glide mode, which speeds things up by delivering more flashes on larger areas of the body (such as the legs), while the stamp mode is ideal for small, more sensitive areas.

The specially trained Auto Mode generates complete 3D heads from images. Design realtime 3D head models and also get separated hair meshes. This intelligent process automatically removes hair from images, provides natural side texture, and is tolerant to face shadows. Auto Mode is suitable to create mid-range crowds for film, or NPC characters in 3D games.

FaceNet v2.0 model was trained on a proprietary dataset with more than 1.8M faces. The training dataset consists of images taken from cameras mounted at varied heights and angles, cameras of varied field-of view (FOV) and occlusions.

3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face ...

A new 3D face recognition framework using keypoints and local geometric features.Robust face recognition which is insensitive to various facial expressions.Accurate recognition using global similarity and local features between faces.State-of-the-art ...

It is well recognized that expressions can significantly change facial geometry that results in a severe problem for robust 3D face recognition. So it is crucial for many applications that how to extract expression-robust features to describe 3D faces. ...

Analyze and return the one or more specified face attributes in the comma-separated string like "returnFaceAttributes=headPose,glasses". Supported face attributes include headPose, glasses, occlusion, accessories, blur, exposure, noise, mask, and qualityForRecognition. Face attribute analysis has additional computational and time cost.

The 'recognitionModel' associated with the detected faceIds. Supported 'recognitionModel' values include "recognition_01", "recognition_02", "recognition_03" and "recognition_04". The default value is "recognition_01". "recognition_04" is recommended since its accuracy is improved on faces wearing masks compared with "recognition_03", and its overall accuracy is improved compared with "recognition_01" and "recognition_02".

A successful call returns an array of face entries ranked by face rectangle size in descending order. An empty response indicatesno faces detected. A face entry may contain the following values depending on input parameters:

Methods:  We did a multicentre, randomised, double-blind, phase 2 study for adult patients with vitiligo in 26 US hospitals and medical centres in 18 states. Patients with depigmentation of 05% or more of their facial body surface area (BSA) and 3% or more of their non-facial BSA were randomly assigned (1:1:1:1:1) by use of an interactive response technology system to receive ruxolitinib cream (15% twice daily, 15% once daily, 05% once daily, or 015% once daily) or vehicle (control group) twice daily on lesions constituting 20% or less of their total BSA for 24 weeks. Patients in the control group in addition to patients in the 015% once daily group who did not show a 25% or higher improvement from baseline in facial Vitiligo Area Scoring Index (F-VASI) at week 24 were re-randomised to one of three higher ruxolitinib cream doses (05% once daily, 15% once daily, 15% twice daily). Patients in the 05% once daily, 15% once daily, or 15% twice daily groups remained at their original dose up to week 52. Patients, investigators, and the study sponsor (except members of the interim analysis and primary endpoint analysis data monitoring teams) remained masked to treatment assignment throughout the study. The primary endpoint was the proportion of patients achieving a 50% or higher improvement from baseline in F-VASI (F-VASI50) at week 24, assessed in the intention-to-treat population. The study is registered with ClinicalTrials.gov, NCT03099304. be457b7860

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