Human Ear Recognition in 3D

By Hui Chen, Bir Bhanu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

371 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/15a31113ae5a2125ae22124cbfe441d1f60f54d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Kevin W. Bowyer
2. Min Lu
3. Almas Abbasi
4. Mohammed Bennamoun
5. Woo Chaw Seng
6. Yulan Guo
7. Danil V. Prokhorov
8. Ferdous Sohel

In Computer Vision and Pattern Recognition

1. Yulan Guo
2. Min Lu
3. Mohammed Bennamoun
4. Ferdous Sohel
5. Almas Abbasi
6. Woo Chaw Seng
7. J. Unar
8. Jianwei Wan

In Signal Processing

1. Almas Abbasi
2. Woo Chaw Seng
3. J. Unar
4. Ajay Kumar
5. Mohammed Bennamoun
6. Ping Yan
7. Chenye Wu
8. Kevin W. Bowyer

Citations in the corpus

• A Convolutional Learning System for Object Classification in 3-D Lidar Data (2010, methodology): “To make the classification problem independent of absolute sizes and positions, we convert each point cloud representing the object to the data of approximately zero mean and unity standard deviation.”
• A review of biometric technology along with trends and prospects (2014, positive): “Table 2: Summary of facial region modalities: Modality Feature Set Recognition Techniques Datasets Face Distance between eyes, mouth, side of nose, entire face image, corner points, contours, gender, goatee, roundness of face, edge maps, pixel intensity, local and global curvatures PCA [95, 96], LDA”
• 3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey (2014, methodology): “quently used to assess the performance of each phase of a 3D object recognition system [3], [36], [37], [38].”
• Automated human identification using ear imaging (2012, background): “Therefore automated personal identification using ear images has been increasingly studied for possible commercial applications.”
• A Comprehensive Performance Evaluation of 3D Local Feature Descriptors (2015, methodology): “The LSP descriptor is a 2D histogram , which is formed by accumulating points in particular bins along the two dimensions (i.e., the shape index value, and the cosine of the angle between the surface normals), as shown in Fig.”
• Biometric Recognition Using 3D Ear Shape (2007, methodology): “Several researchers have looked at using features from the ear’s appearance in 2D intensity images [6], [16], [5], [27], [17], [10], [11], [23], [31], whereas a smaller number of researchers have looked at using 3D ear shape [8], [ 4 ].”