Invariant Feature Regularization for Fair Face Recognition

Fair face recognition is all about learning invariant feature that generalizes to unseen faces in any demographic group. Unfortunately, face datasets inevitably capture the imbalanced demographic attributes that are ubiquitous in real-world observations, and the model learns biased feature that generalizes poorly in the minority group. We point out that the bias arises due to the confounding demographic attributes, which mislead the model to capture the spurious demographic-specific feature. The confounding effect can only be removed by causal intervention, which requires the confounder annotations. However, such annotations can be prohibitively expensive due to the diversity of the demographic attributes. To tackle this, we propose to generate diverse data partitions iteratively in an unsupervised fashion. Each data partition acts as a self-annotated confounder, enabling our Invariant Feature Regularization (INV-REG) to deconfound. INV-REG is orthogonal to existing methods, and combining INV-REG with two strong baselines (Arcface and CIFP) leads to new state-of-the-art that improves face recognition on a variety of demographic groups. Code is available at https://github.com/PanasonicConnect/InvReg.Comment: Accepted by International Conference on Computer Vision (ICCV) 202

Finding faces in wavelet domain for content-based coding of color images

Human face images form the important database in police departments, banks, security kiosks, and they are also found in abundance in day-to-day life. In these databases the important content, of course, is the face region. We present a highly efficient system that detects the human faces in the wavelet transform for discriminative quantization to achieve high perceptual quality content-based image coding technique. The proposed method gives superior subjective performance over JPEG without sacrificing the performance in the rate-distortion spectrum.© IEE

Content based video compression for high perceptual quality videoconferencing using wavelet transform

A novel hybrid compression scheme for videoconferencing and videotelephony applications at very low bit rates (i.e., 32 Kbits/s) is presented. The human face is the most important region within a frame and should be coded with high fidelity. To preserve perceptually important information at low bit rates, such as face regions, skin-tone is used to detect and adaptively quantize these regions. Novel features of this coder are the use of overlapping block motion compensation in combination with discrete wavelet transform, followed by zerotree entropy coding with new scanning procedure of wavelet blocks such that the rest of the H.263 framework can be used. At the same total bit-rate, coarser quantization of the background enables the face region to be quantized finely and coded with higher quality.© IEE

Content based very low bit rate video coding using wavelet transform

In this paper we present a new hybrid compression scheme for videoconferencing and videotelephony applications at very low bit rates (i.e., 32 kbits/s). In these applications, human face is the most important region within a frame and should be coded with high fidelity. To preserve perceptually important information at low bit rates, such as face regions, skin-tone is used to detect and adaptively quantize these regions. Novel features of this coder are the use of overlapping block motion compensation in combination with discrete wavelet transform, followed by zerotree entropy coding with new scanning procedure of wavelet blocks such that the rest of the H.263 framework can be used. At the same total bit-rate, coarser quantization of the background enables the face region to be quantized finely and coded with higher quality. The simulation results demonstrates comparable objective and superior subjective performance when compared with H.263 video coding standard, while providing the advanced feature like scalability functionalities.© IEE

New multiresolution motion estimation and compensation scheme

In this paper, we propose a new algorithm to improve multiresolution motion estimation (MRME) for low bit-rate applications. In this algorithm, a new multiresolution sum of absolute difference (MSAD) error criterion is introduced which reduces the prediction error between two blocks without increasing the number of motion vectors in the wavelet domain. The performance of the algorithm is compared with some other existing techniques.© IEE

SPIHT video coder

In this paper, we propose a new video coding scheme based on discrete wavelet transform (DWT), as it provides a better way to address scalability functionalities, than MPEG-2. To code wavelet coefficients efficiently, we use set partitioning in hierarchical trees (SPIHT) and adaptive arithmetic coding algorithms. Motion compensation (MC) is done in spatial domain to remove temporal redundancy present between frames. To avoid blocking artifacts caused by block motion compensation, overlapping block motion compensation (OBMC) is done. The proposed video encoder is compared with MPEG-2.© IEE

Finding faces in color images using wavelet transform

In this paper, a new fast method for detecting human faces in color images using the wavelet transform is proposed. The face detection algorithm has three stages, where chrominance, shape and frequency information are used respectively. The algorithm starts at the lower resolution version of the image obtained from the wavelet transform, so that the amount of data to be processed is greatly reduced. Experimental results show that the human face in color images can be detected regardless of orientation.© IEE