Spatio-Temporal Pyramidal Deep Learning in Emotion Recognition

EmoP3D is a pyramidal neural network for emotion recognition, based on biological pyramidal neurons developed in [1];

Code for Poster Session at IWCV18:

Di Nardo E., Petrosino A., Ullah I. - Spatio-Temporal Pyramidal Deep Learning in Emotion Recognition. Poster presented at: International Workshop on Computer Vision (IWCV); 2018, May 29-31; Modena, Italy.

Datasets & Results

Code is tested on eNTERFACE '05 [2] and ICT Youtube [3] datasets;

• Faces are extracted using FaceNet.
• Number of feature maps used is 3;
• Samples have 16 frame depth with an overlap of 8 frames i.e 16x100x100x1 depth, height, width, channels dimensions;
• 10-Fold Cross-Validation;
• Mini-batch gradient descent with Momentum optimization:
• Batch size of 100 samples;
• Momentum decay: 0.9;
• Learning rate of 0.00015 and exponential decay ratio of 0.1 every 15 epochs;
• Leaky ReLU activation function;

Results

Model	eNTERFACE	ICT Youtube
AVER-Geometric [4]	41.59%	-
KCMFA [5]	58%	-
AVER-CNN [4]	62%	-
LSTM(A) Binary [6]	-	52.3%
MARN [7]	-	54.2%
EmoP3D (Ours)	71.47%	75%

Usage

Code uses Sparse Softmax Cross Entropy as loss function, it doesn't need One Hot Encoding.

Dependencies

• Tensorflow 1.2+;
• TQDM;
• Numpy;
• Packaging;

FLAGS

Checkpoint and evaluation

Option	Type	Default	Description
evaluate_every	float	1	Number of epoch for each evaluation (decimals allowed)
test_milestones	list	15,25,50	Each epoch where performs test
save_checkpoint	boolean	False	Flag to save checkpoint or not
checkpoint_name	string	3dpyranet.ckpt	Name of checkpoint file

Input

Option	Type	Default	Description
train_path	string	//	Path to npy training set
train_labels_path	string	//	Path to npy training set labels
val_path	string	//	Path to npy val/test set
val_labels_path	string	//	Path to npy val/test set labels
save_path	string	//	Path where to save network model

Input parameters

Option	Type	Default	Description
batch_size	int	100	Batch size
depth_frames	int	16	Number of consecutive samples
height	int	100	Sample height
width	int	100	Sample width
in_channels	int	1	Sample channels
num_classes	int	6	Number of classes

Hyper-parameters settings

Option	Type	Default	Description
feature_maps	int	3	Number of maps to use (strict model shares the number of maps in each layer)
learning_rate	float	0.00015	Learning rate
decay_steps	int	15	Number of epoch for each decay
decay_rate	float	0.1	Learning rate decay
max_steps	int	50	Maximum number of epoch to perform
weight_decay	float	None	L2 regularization lambda

Optimization

Option	Type	Default	Description
optimizer	string	MOMENTUM	Optimization algorthim (GD - MOMENTUM - ADAM)
use_nesterov	boolean	False	Flag to use Nesterov Momentum (it works only with MOMENTUM optimizer)

References

[1] Ullah, Ihsan, and Alfredo Petrosino. "Spatiotemporal features learning with 3DPyraNet." International Conference on Advanced Concepts for Intelligent Vision Systems. Springer, Cham, 2016.

[2] Martin, Olivier, et al. "The enterface’05 audio-visual emotion database." Data Engineering Workshops, 2006. Proceedings. 22nd International Conference on. IEEE, 2006.

[3] Morency, Louis-Philippe, Rada Mihalcea, and Payal Doshi. "Towards multimodal sentiment analysis: Harvesting opinions from the web." Proceedings of the 13th international conference on multimodal interfaces. ACM, 2011.

[4] Noroozi, F., Marjanovic, M., Njegus, A., Escalera, S., and Anbarjafari, G. (2017). Audio-visual emotion recognition in video clips. IEEE Transactions on Affective Computing.

[5] Wang, Y., Guan, L., and Venetsanopoulos, A. N. (2012). Kernel cross-modal factor analysis for information fusion with application to bimodal emotion recognition. IEEE Transactions on Multimedia, 14(3):597–607

[6] Chen, M., Wang, S., Liang, P. P., Baltrušaitis, T., Zadeh, A., and Morency, L.-P. (2017). Multimodal sentiment analysis with word-level fusion and reinforcement learning.

[7] Zadeh, A., Liang, P. P., Poria, S., Vij, P., Cambria, E., and Morency, L.-P. (2018). Multiattention recurrent network for human communication comprehension. arXiv preprint arXiv:1802.00923.