Facial Landmark Detection with Tweaked Convolutional Neural Networks

نویسندگان

  • Yue Wu
  • Tal Hassner
چکیده

We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression. We examine the intermediate features of a standard CNN trained for landmark detection and show that features extracted from later, more specialized layers capture rough landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for landmark detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face landmark detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving Facial Landmark Detection via a Super-Resolution Inception Network

Modern convolutional neural networks for facial landmark detection have become increasingly robust against occlusions, lighting conditions and pose variations. With the predictions being close to pixelaccurate in some cases, intuitively, the input resolution should be as high as possible. We verify this intuition by thoroughly analyzing the impact of low image resolution on landmark prediction ...

متن کامل

Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Hand posture estimation attracts researchers because of its many applications. Hand posture recognition systems simulate the hand postures by using mathematical algorithms. Convolutional neural networks have provided the best results in the hand posture recognition so far. In this paper, we propose a new method to estimate the hand skeletal posture by using deep convolutional neural networks. T...

متن کامل

Predicting Personal Traits from Facial Images Using Convolutional Neural Networks Augmented with Facial Landmark Information

We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labe...

متن کامل

Introducing a method for extracting features from facial images based on applying transformations to features obtained from convolutional neural networks

In pattern recognition, features are denoting some measurable characteristics of an observed phenomenon and feature extraction is the procedure of measuring these characteristics. A set of features can be expressed by a feature vector which is used as the input data of a system. An efficient feature extraction method can improve the performance of a machine learning system such as face recognit...

متن کامل

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs). We first compare and analyse different objective functions and show that the L1 and smooth L1 loss functions perform much better than the widely used L2 loss function in facial landmark localisation. The analysis of these loss functions suggests that, for the trai...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1511.04031  شماره 

صفحات  -

تاریخ انتشار 2015