17 research outputs found
On the Audio-Visual Emotion Recognition using Convolutional Neural Networks and Extreme Learning Machine
The advances in artificial intelligence and machine learning concerning emotion recognition have been enormous and in previously inconceivable ways. Inspired by the promising evolution in human-computer interaction, this paper is based on developing a multimodal emotion recognition system. This research encompasses two modalities as input, namely speech and video. In the proposed model, the input video samples are subjected to image pre-processing and image frames are obtained. The signal is pre-processed and transformed into the frequency domain for the audio input. The aim is to obtain Mel-spectrogram, which is processed further as images. Convolutional neural networks are used for training and feature extraction for both audio and video with different configurations. The fusion of outputs from two CNNs is done using two extreme learning machines. For classification, the proposed system incorporates a support vector machine. The model is evaluated using three databases, namely eNTERFACE, RML, and SAVEE. For the eNTERFACE dataset, the accuracy obtained without and with augmentation was 87.2% and 94.91%, respectively. The RML dataset yielded an accuracy of 98.5%, and for the SAVEE dataset, the accuracy reached 97.77%. Results achieved from this research are an illustration of the fruitful exploration and effectiveness of the proposed system
Meta Transfer Learning for Facial Emotion Recognition
The use of deep learning techniques for automatic facial expression
recognition has recently attracted great interest but developed models are
still unable to generalize well due to the lack of large emotion datasets for
deep learning. To overcome this problem, in this paper, we propose utilizing a
novel transfer learning approach relying on PathNet and investigate how
knowledge can be accumulated within a given dataset and how the knowledge
captured from one emotion dataset can be transferred into another in order to
improve the overall performance. To evaluate the robustness of our system, we
have conducted various sets of experiments on two emotion datasets: SAVEE and
eNTERFACE. The experimental results demonstrate that our proposed system leads
to improvement in performance of emotion recognition and performs significantly
better than the recent state-of-the-art schemes adopting fine-\
tuning/pre-trained approaches
Deep Architectures and Ensembles for Semantic Video Classification
This work addresses the problem of accurate semantic labelling of short
videos. To this end, a multitude of different deep nets, ranging from
traditional recurrent neural networks (LSTM, GRU), temporal agnostic networks
(FV,VLAD,BoW), fully connected neural networks mid-stage AV fusion and others.
Additionally, we also propose a residual architecture-based DNN for video
classification, with state-of-the art classification performance at
significantly reduced complexity. Furthermore, we propose four new approaches
to diversity-driven multi-net ensembling, one based on fast correlation measure
and three incorporating a DNN-based combiner. We show that significant
performance gains can be achieved by ensembling diverse nets and we investigate
factors contributing to high diversity. Based on the extensive YouTube8M
dataset, we provide an in-depth evaluation and analysis of their behaviour. We
show that the performance of the ensemble is state-of-the-art achieving the
highest accuracy on the YouTube-8M Kaggle test data. The performance of the
ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets,
and show that the resulting method achieves comparable accuracy with
state-of-the-art methods using similar input features